Import all the Libraries

import pandas as pd
import seaborn as sb
import matplotlib.pyplot as plt
from matplotlib.pyplot import show

df=pd.read_csv('Downloads/training_set.csv')

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1460 entries, 0 to 1459
Data columns (total 81 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   Id             1460 non-null   int64  
 1   MSSubClass     1460 non-null   int64  
 2   MSZoning       1460 non-null   object 
 3   LotFrontage    1201 non-null   float64
 4   LotArea        1460 non-null   int64  
 5   Street         1460 non-null   object 
 6   Alley          91 non-null     object 
 7   LotShape       1460 non-null   object 
 8   LandContour    1460 non-null   object 
 9   Utilities      1460 non-null   object 
 10  LotConfig      1460 non-null   object 
 11  LandSlope      1460 non-null   object 
 12  Neighborhood   1460 non-null   object 
 13  Condition1     1460 non-null   object 
 14  Condition2     1460 non-null   object 
 15  BldgType       1460 non-null   object 
 16  HouseStyle     1460 non-null   object 
 17  OverallQual    1460 non-null   int64  
 18  OverallCond    1460 non-null   int64  
 19  YearBuilt      1460 non-null   int64  
 20  YearRemodAdd   1460 non-null   int64  
 21  RoofStyle      1460 non-null   object 
 22  RoofMatl       1460 non-null   object 
 23  Exterior1st    1460 non-null   object 
 24  Exterior2nd    1460 non-null   object 
 25  MasVnrType     1452 non-null   object 
 26  MasVnrArea     1452 non-null   float64
 27  ExterQual      1460 non-null   object 
 28  ExterCond      1460 non-null   object 
 29  Foundation     1460 non-null   object 
 30  BsmtQual       1423 non-null   object 
 31  BsmtCond       1423 non-null   object 
 32  BsmtExposure   1422 non-null   object 
 33  BsmtFinType1   1423 non-null   object 
 34  BsmtFinSF1     1460 non-null   int64  
 35  BsmtFinType2   1422 non-null   object 
 36  BsmtFinSF2     1460 non-null   int64  
 37  BsmtUnfSF      1460 non-null   int64  
 38  TotalBsmtSF    1460 non-null   int64  
 39  Heating        1460 non-null   object 
 40  HeatingQC      1460 non-null   object 
 41  CentralAir     1460 non-null   object 
 42  Electrical     1459 non-null   object 
 43  1stFlrSF       1460 non-null   int64  
 44  2ndFlrSF       1460 non-null   int64  
 45  LowQualFinSF   1460 non-null   int64  
 46  GrLivArea      1460 non-null   int64  
 47  BsmtFullBath   1460 non-null   int64  
 48  BsmtHalfBath   1460 non-null   int64  
 49  FullBath       1460 non-null   int64  
 50  HalfBath       1460 non-null   int64  
 51  BedroomAbvGr   1460 non-null   int64  
 52  KitchenAbvGr   1460 non-null   int64  
 53  KitchenQual    1460 non-null   object 
 54  TotRmsAbvGrd   1460 non-null   int64  
 55  Functional     1460 non-null   object 
 56  Fireplaces     1460 non-null   int64  
 57  FireplaceQu    770 non-null    object 
 58  GarageType     1379 non-null   object 
 59  GarageYrBlt    1379 non-null   float64
 60  GarageFinish   1379 non-null   object 
 61  GarageCars     1460 non-null   int64  
 62  GarageArea     1460 non-null   int64  
 63  GarageQual     1379 non-null   object 
 64  GarageCond     1379 non-null   object 
 65  PavedDrive     1460 non-null   object 
 66  WoodDeckSF     1460 non-null   int64  
 67  OpenPorchSF    1460 non-null   int64  
 68  EnclosedPorch  1460 non-null   int64  
 69  3SsnPorch      1460 non-null   int64  
 70  ScreenPorch    1460 non-null   int64  
 71  PoolArea       1460 non-null   int64  
 72  PoolQC         7 non-null      object 
 73  Fence          281 non-null    object 
 74  MiscFeature    54 non-null     object 
 75  MiscVal        1460 non-null   int64  
 76  MoSold         1460 non-null   int64  
 77  YrSold         1460 non-null   int64  
 78  SaleType       1460 non-null   object 
 79  SaleCondition  1460 non-null   object 
 80  SalePrice      1460 non-null   int64  
dtypes: float64(3), int64(35), object(43)
memory usage: 924.0+ KB

df['SaleCondition'].value_counts()

Normal     1198
Partial     125
Abnorml     101
Family       20
Alloca       12
AdjLand       4
Name: SaleCondition, dtype: int64

df.shape

(1460, 81)

Check whether there are any Null values

df.isna().sum()

Id                 0
MSSubClass         0
MSZoning           0
LotFrontage      259
LotArea            0
                ... 
MoSold             0
YrSold             0
SaleType           0
SaleCondition      0
SalePrice          0
Length: 81, dtype: int64

Filling Null values

for i in df.columns:
    if (df[i].isna().sum())>0:
        if df[i].dtypes=='object':
            x=df[i].mode()[0]
            df[i]=df[i].fillna(x)
        else:
            x=df[i].mean()
            df[i]=df[i].fillna(x)

df.isna().sum()

Id               0
MSSubClass       0
MSZoning         0
LotFrontage      0
LotArea          0
                ..
MoSold           0
YrSold           0
SaleType         0
SaleCondition    0
SalePrice        0
Length: 81, dtype: int64

Drop irrelevant columns [Seperating Input Output features (X & Y)]

X=df.drop(labels=['SalePrice','Id','LowQualFinSF','MiscVal'],axis=1)
Y=df['SalePrice']

X.shape

(1460, 77)

Y.shape

(1460,)

Seperating Categorical & Continuous columns

cat=[]
con=[]
for i in X.columns:
    if X[i].dtypes=='object':
        cat.append(i)
    else:
        con.append(i)
print(cat)
print(con)

['MSZoning', 'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual', 'Functional', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'PavedDrive', 'PoolQC', 'Fence', 'MiscFeature', 'SaleType', 'SaleCondition']
['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MoSold', 'YrSold']

cat=['MSZoning', 'Street', 'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual', 'Functional', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'PavedDrive', 'Fence', 'SaleType', 'SaleCondition']
con=['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'MoSold', 'YrSold']

Analysis

UNIVARIATE ANALYSIS (Distplot)

for i in df.columns:
    if df[i].dtypes=='object':
        df[i].value_counts().plot(kind='bar')
        plt.show()
    else:
        sb.histplot(data=df,x=df[i],kde=True)
        plt.show()

BIVARIATE ANALYSIS (Scatter Plot)

for i in df.columns:
    if df[i].dtypes=='object':
        sb.boxplot(data=df,x=df[i],y='SalePrice')
        plt.show()
    else:
        plt.scatter(data=df,x=df[i],y='SalePrice')
        plt.xlabel(i)
        plt.ylabel('SalePrice')
        plt.title(f'{i} vs SalePrice')
        plt.show()

MULTIVARIATE ANALYSIS (Heatmap)

a=df.corr()

C:\Users\PRATHMESH\AppData\Local\Temp\ipykernel_288228\962033958.py:1: FutureWarning: The default value of numeric_only in DataFrame.corr is deprecated. In a future version, it will default to False. Select only valid columns or specify the value of numeric_only to silence this warning.
  a=df.corr()

a

	Id	MSSubClass	LotFrontage	LotArea	OverallQual	OverallCond	YearBuilt	YearRemodAdd	MasVnrArea	BsmtFinSF1	...	WoodDeckSF	OpenPorchSF	EnclosedPorch	3SsnPorch	ScreenPorch	PoolArea	MiscVal	MoSold	YrSold	SalePrice
Id	1.000000	0.011156	-0.009601	-0.033226	-0.028365	0.012609	-0.012713	-0.021998	-0.050199	-0.005024	...	-0.029643	-0.000477	0.002889	-0.046635	0.001330	0.057044	-0.006242	0.021172	0.000712	-0.021917
MSSubClass	0.011156	1.000000	-0.357056	-0.139781	0.032628	-0.059316	0.027850	0.040581	0.022895	-0.069836	...	-0.012579	-0.006100	-0.012037	-0.043825	-0.026030	0.008283	-0.007683	-0.013585	-0.021407	-0.084284
LotFrontage	-0.009601	-0.357056	1.000000	0.306795	0.234196	-0.052820	0.117598	0.082746	0.179283	0.215828	...	0.077106	0.137454	0.009790	0.062335	0.037684	0.180868	0.001168	0.010158	0.006768	0.334901
LotArea	-0.033226	-0.139781	0.306795	1.000000	0.105806	-0.005636	0.014228	0.013788	0.103960	0.214103	...	0.171698	0.084774	-0.018340	0.020423	0.043160	0.077672	0.038068	0.001205	-0.014261	0.263843
OverallQual	-0.028365	0.032628	0.234196	0.105806	1.000000	-0.091932	0.572323	0.550684	0.410238	0.239666	...	0.238923	0.308819	-0.113937	0.030371	0.064886	0.065166	-0.031406	0.070815	-0.027347	0.790982
OverallCond	0.012609	-0.059316	-0.052820	-0.005636	-0.091932	1.000000	-0.375983	0.073741	-0.127788	-0.046231	...	-0.003334	-0.032589	0.070356	0.025504	0.054811	-0.001985	0.068777	-0.003511	0.043950	-0.077856
YearBuilt	-0.012713	0.027850	0.117598	0.014228	0.572323	-0.375983	1.000000	0.592855	0.314745	0.249503	...	0.224880	0.188686	-0.387268	0.031355	-0.050364	0.004950	-0.034383	0.012398	-0.013618	0.522897
YearRemodAdd	-0.021998	0.040581	0.082746	0.013788	0.550684	0.073741	0.592855	1.000000	0.179186	0.128451	...	0.205726	0.226298	-0.193919	0.045286	-0.038740	0.005829	-0.010286	0.021490	0.035743	0.507101
MasVnrArea	-0.050199	0.022895	0.179283	0.103960	0.410238	-0.127788	0.314745	0.179186	1.000000	0.263582	...	0.159349	0.124965	-0.109849	0.018795	0.061453	0.011723	-0.029815	-0.005940	-0.008184	0.475241
BsmtFinSF1	-0.005024	-0.069836	0.215828	0.214103	0.239666	-0.046231	0.249503	0.128451	0.263582	1.000000	...	0.204306	0.111761	-0.102303	0.026451	0.062021	0.140491	0.003571	-0.015727	0.014359	0.386420
BsmtFinSF2	-0.005968	-0.065649	0.043340	0.111170	-0.059119	0.040229	-0.049107	-0.067759	-0.072302	-0.050117	...	0.067898	0.003093	0.036543	-0.029993	0.088871	0.041709	0.004940	-0.015211	0.031706	-0.011378
BsmtUnfSF	-0.007940	-0.140759	0.122156	-0.002618	0.308159	-0.136841	0.149040	0.181133	0.114184	-0.495251	...	-0.005316	0.129005	-0.002538	0.020764	-0.012579	-0.035092	-0.023837	0.034888	-0.041258	0.214479
TotalBsmtSF	-0.015415	-0.238518	0.363358	0.260833	0.537808	-0.171098	0.391452	0.291066	0.362452	0.522396	...	0.232019	0.247264	-0.095478	0.037384	0.084489	0.126053	-0.018479	0.013196	-0.014969	0.613581
1stFlrSF	0.010496	-0.251758	0.414266	0.299475	0.476224	-0.144203	0.281986	0.240379	0.342160	0.445863	...	0.235459	0.211671	-0.065292	0.056104	0.088758	0.131525	-0.021096	0.031372	-0.013604	0.605852
2ndFlrSF	0.005590	0.307886	0.072483	0.050986	0.295493	0.028942	0.010308	0.140024	0.174019	-0.137079	...	0.092165	0.208026	0.061989	-0.024358	0.040606	0.081487	0.016197	0.035164	-0.028700	0.319334
LowQualFinSF	-0.044230	0.046474	0.036849	0.004779	-0.030429	0.025494	-0.183784	-0.062419	-0.069068	-0.064503	...	-0.025444	0.018251	0.061081	-0.004296	0.026799	0.062157	-0.003793	-0.022174	-0.028921	-0.025606
GrLivArea	0.008273	0.074853	0.368392	0.263116	0.593007	-0.079686	0.199010	0.287389	0.389893	0.208171	...	0.247433	0.330224	0.009113	0.020643	0.101510	0.170205	-0.002416	0.050240	-0.036526	0.708624
BsmtFullBath	0.002289	0.003491	0.091481	0.158155	0.111098	-0.054942	0.187599	0.119470	0.085055	0.649212	...	0.175315	0.067341	-0.049911	-0.000106	0.023148	0.067616	-0.023047	-0.025361	0.067049	0.227122
BsmtHalfBath	-0.020155	-0.002333	-0.006419	0.048046	-0.040150	0.117821	-0.038162	-0.012337	0.026669	0.067418	...	0.040161	-0.025324	-0.008555	0.035114	0.032121	0.020025	-0.007367	0.032873	-0.046524	-0.016844
FullBath	0.005587	0.131608	0.180424	0.126031	0.550600	-0.194149	0.468271	0.439046	0.275730	0.058543	...	0.187703	0.259977	-0.115093	0.035353	-0.008106	0.049604	-0.014290	0.055872	-0.019669	0.560664
HalfBath	0.006784	0.177354	0.048258	0.014259	0.273458	-0.060769	0.242656	0.183331	0.200802	0.004262	...	0.108080	0.199740	-0.095317	-0.004972	0.072426	0.022381	0.001290	-0.009050	-0.010269	0.284108
BedroomAbvGr	0.037719	-0.023438	0.237023	0.119690	0.101676	0.012980	-0.070651	-0.040581	0.102417	-0.107355	...	0.046854	0.093810	0.041570	-0.024478	0.044300	0.070703	0.007767	0.046544	-0.036014	0.168213
KitchenAbvGr	0.002951	0.281721	-0.005805	-0.017784	-0.183882	-0.087001	-0.174800	-0.149598	-0.037364	-0.081007	...	-0.090130	-0.070091	0.037312	-0.024600	-0.051613	-0.014525	0.062341	0.026589	0.031687	-0.135907
TotRmsAbvGrd	0.027239	0.040380	0.320146	0.190015	0.427452	-0.057583	0.095589	0.191740	0.280027	0.044316	...	0.165984	0.234192	0.004151	-0.006683	0.059383	0.083757	0.024763	0.036907	-0.034516	0.533723
Fireplaces	-0.019772	-0.045569	0.235755	0.271364	0.396765	-0.023820	0.147716	0.112581	0.247906	0.260011	...	0.200019	0.169405	-0.024822	0.011257	0.184530	0.095074	0.001409	0.046357	-0.024096	0.466929
GarageYrBlt	0.000070	0.080187	0.064324	-0.024812	0.518018	-0.306169	0.780555	0.618130	0.249367	0.150338	...	0.220623	0.218490	-0.285882	0.023534	-0.075256	-0.014499	-0.031853	0.005173	-0.000987	0.470177
GarageCars	0.016570	-0.040110	0.269729	0.154871	0.600671	-0.185758	0.537850	0.420622	0.363778	0.224054	...	0.226342	0.213569	-0.151434	0.035765	0.050494	0.020934	-0.043080	0.040522	-0.039117	0.640409
GarageArea	0.017634	-0.098672	0.323663	0.180403	0.562022	-0.151521	0.478954	0.371600	0.372567	0.296970	...	0.224666	0.241435	-0.121777	0.035087	0.051412	0.061047	-0.027400	0.027974	-0.027378	0.623431
WoodDeckSF	-0.029643	-0.012579	0.077106	0.171698	0.238923	-0.003334	0.224880	0.205726	0.159349	0.204306	...	1.000000	0.058661	-0.125989	-0.032771	-0.074181	0.073378	-0.009551	0.021011	0.022270	0.324413
OpenPorchSF	-0.000477	-0.006100	0.137454	0.084774	0.308819	-0.032589	0.188686	0.226298	0.124965	0.111761	...	0.058661	1.000000	-0.093079	-0.005842	0.074304	0.060762	-0.018584	0.071255	-0.057619	0.315856
EnclosedPorch	0.002889	-0.012037	0.009790	-0.018340	-0.113937	0.070356	-0.387268	-0.193919	-0.109849	-0.102303	...	-0.125989	-0.093079	1.000000	-0.037305	-0.082864	0.054203	0.018361	-0.028887	-0.009916	-0.128578
3SsnPorch	-0.046635	-0.043825	0.062335	0.020423	0.030371	0.025504	0.031355	0.045286	0.018795	0.026451	...	-0.032771	-0.005842	-0.037305	1.000000	-0.031436	-0.007992	0.000354	0.029474	0.018645	0.044584
ScreenPorch	0.001330	-0.026030	0.037684	0.043160	0.064886	0.054811	-0.050364	-0.038740	0.061453	0.062021	...	-0.074181	0.074304	-0.082864	-0.031436	1.000000	0.051307	0.031946	0.023217	0.010694	0.111447
PoolArea	0.057044	0.008283	0.180868	0.077672	0.065166	-0.001985	0.004950	0.005829	0.011723	0.140491	...	0.073378	0.060762	0.054203	-0.007992	0.051307	1.000000	0.029669	-0.033737	-0.059689	0.092404
MiscVal	-0.006242	-0.007683	0.001168	0.038068	-0.031406	0.068777	-0.034383	-0.010286	-0.029815	0.003571	...	-0.009551	-0.018584	0.018361	0.000354	0.031946	0.029669	1.000000	-0.006495	0.004906	-0.021190
MoSold	0.021172	-0.013585	0.010158	0.001205	0.070815	-0.003511	0.012398	0.021490	-0.005940	-0.015727	...	0.021011	0.071255	-0.028887	0.029474	0.023217	-0.033737	-0.006495	1.000000	-0.145721	0.046432
YrSold	0.000712	-0.021407	0.006768	-0.014261	-0.027347	0.043950	-0.013618	0.035743	-0.008184	0.014359	...	0.022270	-0.057619	-0.009916	0.018645	0.010694	-0.059689	0.004906	-0.145721	1.000000	-0.028923
SalePrice	-0.021917	-0.084284	0.334901	0.263843	0.790982	-0.077856	0.522897	0.507101	0.475241	0.386420	...	0.324413	0.315856	-0.128578	0.044584	0.111447	0.092404	-0.021190	0.046432	-0.028923	1.000000

38 rows × 38 columns

sb.heatmap(a,annot=True)

<Axes: >

Standardisation of Continuous

from sklearn.preprocessing import StandardScaler

ss=StandardScaler()

X1=pd.DataFrame(ss.fit_transform(X[con]),columns=con)

X1

	MSSubClass	LotFrontage	LotArea	OverallQual	OverallCond	YearBuilt	YearRemodAdd	MasVnrArea	BsmtFinSF1	BsmtFinSF2	...	GarageYrBlt	GarageCars	GarageArea	WoodDeckSF	OpenPorchSF	EnclosedPorch	3SsnPorch	ScreenPorch	MoSold	YrSold
0	0.073375	-0.229372	-0.207142	0.651479	-0.517200	1.050994	0.878668	0.511418	0.575425	-0.288653	...	1.021157	0.311725	0.351000	-0.752176	0.216503	-0.359325	-0.116339	-0.270208	-1.599111	0.138777
1	-0.872563	0.451936	-0.091886	-0.071836	2.179628	0.156734	-0.429577	-0.574410	1.171992	-0.288653	...	-0.104483	0.311725	-0.060731	1.626195	-0.704483	-0.359325	-0.116339	-0.270208	-0.489110	-0.614439
2	0.073375	-0.093110	0.073480	0.651479	-0.517200	0.984752	0.830215	0.323060	0.092907	-0.288653	...	0.937776	0.311725	0.631726	-0.752176	-0.070361	-0.359325	-0.116339	-0.270208	0.990891	0.138777
3	0.309859	-0.456474	-0.096897	0.651479	-0.517200	-1.863632	-0.720298	-0.574410	-0.499274	-0.288653	...	0.812705	1.650307	0.790804	-0.752176	-0.176048	4.092524	-0.116339	-0.270208	-1.599111	-1.367655
4	0.073375	0.633618	0.375148	1.374795	-0.517200	0.951632	0.733308	1.364570	0.463568	-0.288653	...	0.896086	1.650307	1.698485	0.780197	0.563760	-0.359325	-0.116339	-0.270208	2.100892	0.138777
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1455	0.073375	-0.365633	-0.260560	-0.071836	-0.517200	0.918511	0.733308	-0.574410	-0.973018	-0.288653	...	0.854395	0.311725	-0.060731	-0.752176	-0.100558	-0.359325	-0.116339	-0.270208	0.620891	-0.614439
1456	-0.872563	0.679039	0.266407	-0.071836	0.381743	0.222975	0.151865	0.084843	0.759659	0.722112	...	-0.021102	0.311725	0.126420	2.033231	-0.704483	-0.359325	-0.116339	-0.270208	-1.599111	1.645210
1457	0.309859	-0.183951	-0.147810	0.651479	3.078570	-1.002492	1.024029	-0.574410	-0.369871	-0.288653	...	-1.563645	-1.026858	-1.033914	-0.752176	0.201405	-0.359325	-0.116339	-0.270208	-0.489110	1.645210
1458	-0.872563	-0.093110	-0.080160	-0.795151	0.381743	-0.704406	0.539493	-0.574410	-0.865548	6.092188	...	-1.188432	-1.026858	-1.090059	2.168910	-0.704483	1.473789	-0.116339	-0.270208	-0.859110	1.645210
1459	-0.872563	0.224833	-0.058112	-0.795151	0.381743	-0.207594	-0.962566	-0.574410	0.847389	1.509640	...	-0.563077	-1.026858	-0.921624	5.121921	0.322190	-0.359325	-0.116339	-0.270208	-0.119110	0.138777

1460 rows × 33 columns

Encoding ( converting Categorical cloumns in continous)

ONE HOT ENCODING

X2=pd.get_dummies(X[cat])

X2

	MSZoning_C (all)	MSZoning_FV	MSZoning_RH	MSZoning_RL	MSZoning_RM	Street_Grvl	Street_Pave	LotShape_IR1	LotShape_IR2	LotShape_IR3	...	SaleType_ConLw	SaleType_New	SaleType_Oth	SaleType_WD	SaleCondition_Abnorml	SaleCondition_AdjLand	SaleCondition_Alloca	SaleCondition_Family	SaleCondition_Normal	SaleCondition_Partial
0	0	0	0	1	0	0	1	0	0	0	...	0	0	0	1	0	0	0	0	1	0
1	0	0	0	1	0	0	1	0	0	0	...	0	0	0	1	0	0	0	0	1	0
2	0	0	0	1	0	0	1	1	0	0	...	0	0	0	1	0	0	0	0	1	0
3	0	0	0	1	0	0	1	1	0	0	...	0	0	0	1	1	0	0	0	0	0
4	0	0	0	1	0	0	1	1	0	0	...	0	0	0	1	0	0	0	0	1	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1455	0	0	0	1	0	0	1	0	0	0	...	0	0	0	1	0	0	0	0	1	0
1456	0	0	0	1	0	0	1	0	0	0	...	0	0	0	1	0	0	0	0	1	0
1457	0	0	0	1	0	0	1	0	0	0	...	0	0	0	1	0	0	0	0	1	0
1458	0	0	0	1	0	0	1	0	0	0	...	0	0	0	1	0	0	0	0	1	0
1459	0	0	0	1	0	0	1	0	0	0	...	0	0	0	1	0	0	0	0	1	0

1460 rows × 243 columns

X2.columns

Index(['MSZoning_C (all)', 'MSZoning_FV', 'MSZoning_RH', 'MSZoning_RL',
       'MSZoning_RM', 'Street_Grvl', 'Street_Pave', 'LotShape_IR1',
       'LotShape_IR2', 'LotShape_IR3',
       ...
       'SaleType_ConLw', 'SaleType_New', 'SaleType_Oth', 'SaleType_WD',
       'SaleCondition_Abnorml', 'SaleCondition_AdjLand',
       'SaleCondition_Alloca', 'SaleCondition_Family', 'SaleCondition_Normal',
       'SaleCondition_Partial'],
      dtype='object', length=243)

df.SaleCondition.value_counts()

Normal     1198
Partial     125
Abnorml     101
Family       20
Alloca       12
AdjLand       4
Name: SaleCondition, dtype: int64

DETECTING & TREATING OUTLIERS

out=[]
for i in con:
    out.extend((X1[(X1[i]>3) | (X1[i]<-3)]).index)
print(out)

[9, 48, 93, 125, 165, 246, 291, 300, 312, 335, 411, 488, 520, 535, 635, 637, 703, 705, 713, 861, 969, 985, 1030, 1062, 1144, 1186, 1190, 1266, 1393, 1416, 171, 197, 231, 277, 313, 446, 807, 909, 934, 1107, 1127, 1173, 1182, 1211, 1298, 1337, 53, 249, 313, 335, 384, 451, 457, 661, 706, 769, 848, 1298, 1396, 375, 533, 88, 185, 191, 218, 241, 250, 304, 375, 378, 398, 461, 508, 519, 583, 676, 703, 726, 745, 980, 991, 1123, 1149, 1213, 1268, 1327, 1352, 1435, 1457, 304, 630, 747, 1132, 1137, 1349, 37, 58, 70, 105, 115, 161, 169, 178, 224, 297, 349, 403, 477, 517, 523, 654, 691, 718, 755, 763, 798, 808, 825, 898, 981, 1111, 1169, 1228, 1289, 1298, 1373, 1417, 70, 178, 523, 898, 1182, 1298, 24, 52, 113, 116, 153, 166, 233, 253, 260, 263, 271, 273, 313, 322, 355, 414, 440, 446, 470, 493, 542, 548, 577, 586, 599, 666, 697, 764, 785, 828, 842, 854, 888, 918, 923, 924, 1040, 1059, 1077, 1152, 1220, 1253, 1299, 1308, 1320, 1369, 1387, 1418, 1445, 1458, 137, 224, 278, 477, 496, 581, 678, 774, 798, 932, 1267, 224, 332, 440, 496, 523, 691, 1044, 1182, 1298, 1373, 224, 440, 496, 523, 529, 691, 898, 1024, 1044, 1182, 1298, 1373, 304, 691, 1169, 1182, 118, 185, 197, 304, 496, 523, 608, 635, 691, 769, 798, 1169, 1182, 1268, 1298, 1353, 53, 188, 313, 326, 335, 420, 454, 588, 634, 738, 807, 921, 942, 1163, 1270, 1298, 1, 26, 33, 37, 41, 50, 93, 116, 129, 176, 197, 201, 213, 215, 218, 245, 249, 251, 253, 298, 299, 314, 330, 352, 358, 367, 414, 421, 426, 499, 504, 558, 574, 576, 580, 597, 611, 628, 633, 658, 691, 697, 717, 741, 743, 745, 814, 828, 892, 920, 925, 931, 944, 952, 953, 954, 1006, 1029, 1041, 1047, 1052, 1055, 1069, 1072, 1076, 1080, 1103, 1118, 1123, 1149, 1156, 1181, 1213, 1225, 1276, 1287, 1327, 1335, 1350, 1389, 1405, 1415, 188, 298, 597, 624, 628, 921, 1154, 1163, 1230, 1283, 1350, 1450, 53, 144, 189, 291, 330, 570, 634, 635, 843, 897, 1163, 1213, 1270, 1350, 8, 9, 17, 39, 48, 74, 78, 93, 102, 137, 144, 165, 188, 246, 330, 342, 420, 441, 454, 488, 505, 520, 529, 570, 634, 635, 637, 676, 703, 705, 728, 736, 778, 809, 843, 886, 894, 897, 910, 913, 921, 940, 942, 943, 954, 955, 984, 1003, 1011, 1030, 1062, 1090, 1163, 1186, 1216, 1230, 1232, 1266, 1275, 1283, 1292, 1336, 1350, 1391, 1393, 1412, 1416, 1450, 185, 635, 769, 803, 897, 910, 1031, 1173, 1230, 1298, 1350, 1386, 166, 309, 605, 642, 1298, 93, 653, 178, 581, 664, 825, 1061, 1190, 1298, 53, 64, 166, 169, 335, 343, 357, 480, 661, 769, 828, 848, 893, 961, 974, 1044, 1068, 1210, 1312, 1313, 1423, 1459, 28, 185, 293, 495, 499, 523, 583, 591, 645, 664, 666, 713, 735, 775, 784, 807, 854, 875, 947, 961, 996, 1184, 1193, 1292, 1298, 1328, 1369, 3, 7, 154, 197, 260, 306, 314, 325, 328, 358, 365, 380, 459, 462, 496, 520, 577, 630, 648, 653, 660, 662, 718, 720, 747, 799, 813, 836, 840, 918, 939, 945, 1013, 1030, 1081, 1119, 1139, 1150, 1152, 1185, 1197, 1202, 1248, 1266, 1326, 1360, 1382, 1393, 1419, 1439, 1445, 5, 55, 120, 129, 159, 182, 187, 205, 237, 258, 280, 546, 704, 726, 744, 889, 941, 1080, 1156, 1161, 1181, 1346, 1437, 46, 72, 80, 104, 176, 185, 189, 196, 289, 297, 312, 339, 351, 359, 360, 366, 400, 426, 471, 475, 550, 605, 618, 625, 647, 673, 764, 769, 785, 795, 803, 828, 830, 854, 859, 887, 888, 907, 919, 944, 1037, 1055, 1067, 1070, 1154, 1171, 1184, 1228, 1282, 1293, 1301, 1320, 1328, 1386, 1414]

import numpy as np

outliers=np.unique(out)

outliers

array([   1,    3,    5,    7,    8,    9,   17,   24,   26,   28,   33,
         37,   39,   41,   46,   48,   50,   52,   53,   55,   58,   64,
         70,   72,   74,   78,   80,   88,   93,  102,  104,  105,  113,
        115,  116,  118,  120,  125,  129,  137,  144,  153,  154,  159,
        161,  165,  166,  169,  171,  176,  178,  182,  185,  187,  188,
        189,  191,  196,  197,  201,  205,  213,  215,  218,  224,  231,
        233,  237,  241,  245,  246,  249,  250,  251,  253,  258,  260,
        263,  271,  273,  277,  278,  280,  289,  291,  293,  297,  298,
        299,  300,  304,  306,  309,  312,  313,  314,  322,  325,  326,
        328,  330,  332,  335,  339,  342,  343,  349,  351,  352,  355,
        357,  358,  359,  360,  365,  366,  367,  375,  378,  380,  384,
        398,  400,  403,  411,  414,  420,  421,  426,  440,  441,  446,
        451,  454,  457,  459,  461,  462,  470,  471,  475,  477,  480,
        488,  493,  495,  496,  499,  504,  505,  508,  517,  519,  520,
        523,  529,  533,  535,  542,  546,  548,  550,  558,  570,  574,
        576,  577,  580,  581,  583,  586,  588,  591,  597,  599,  605,
        608,  611,  618,  624,  625,  628,  630,  633,  634,  635,  637,
        642,  645,  647,  648,  653,  654,  658,  660,  661,  662,  664,
        666,  673,  676,  678,  691,  697,  703,  704,  705,  706,  713,
        717,  718,  720,  726,  728,  735,  736,  738,  741,  743,  744,
        745,  747,  755,  763,  764,  769,  774,  775,  778,  784,  785,
        795,  798,  799,  803,  807,  808,  809,  813,  814,  825,  828,
        830,  836,  840,  842,  843,  848,  854,  859,  861,  875,  886,
        887,  888,  889,  892,  893,  894,  897,  898,  907,  909,  910,
        913,  918,  919,  920,  921,  923,  924,  925,  931,  932,  934,
        939,  940,  941,  942,  943,  944,  945,  947,  952,  953,  954,
        955,  961,  969,  974,  980,  981,  984,  985,  991,  996, 1003,
       1006, 1011, 1013, 1024, 1029, 1030, 1031, 1037, 1040, 1041, 1044,
       1047, 1052, 1055, 1059, 1061, 1062, 1067, 1068, 1069, 1070, 1072,
       1076, 1077, 1080, 1081, 1090, 1103, 1107, 1111, 1118, 1119, 1123,
       1127, 1132, 1137, 1139, 1144, 1149, 1150, 1152, 1154, 1156, 1161,
       1163, 1169, 1171, 1173, 1181, 1182, 1184, 1185, 1186, 1190, 1193,
       1197, 1202, 1210, 1211, 1213, 1216, 1220, 1225, 1228, 1230, 1232,
       1248, 1253, 1266, 1267, 1268, 1270, 1275, 1276, 1282, 1283, 1287,
       1289, 1292, 1293, 1298, 1299, 1301, 1308, 1312, 1313, 1320, 1326,
       1327, 1328, 1335, 1336, 1337, 1346, 1349, 1350, 1352, 1353, 1360,
       1369, 1373, 1382, 1386, 1387, 1389, 1391, 1393, 1396, 1405, 1412,
       1414, 1415, 1416, 1417, 1418, 1419, 1423, 1435, 1437, 1439, 1445,
       1450, 1457, 1458, 1459])

Droping Outliers from X & Y

X1.drop(index=outliers,axis=0,inplace=True)
X2.drop(index=outliers,axis=0,inplace=True)
Y.drop(index=outliers,axis=0,inplace=True)

X1.shape

(1038, 33)

X2.shape

(1038, 243)

Y.shape

(1038,)

Join Cat & Con

Xnew=X1.join(X2)

Xnew.shape

(1038, 276)

Reindexing X & Y

Xnew.index=range(0,1038)
Y.index=range(0,1038)

Xnew.head()

	MSSubClass	LotFrontage	LotArea	OverallQual	OverallCond	YearBuilt	YearRemodAdd	MasVnrArea	BsmtFinSF1	BsmtFinSF2	...	SaleType_ConLw	SaleType_New	SaleType_Oth	SaleType_WD	SaleCondition_Abnorml	SaleCondition_AdjLand	SaleCondition_Alloca	SaleCondition_Family	SaleCondition_Normal	SaleCondition_Partial
0	0.073375	-0.229372	-0.207142	0.651479	-0.5172	1.050994	0.878668	0.511418	0.575425	-0.288653	...	0	0	0	1	0	0	0	0	1	0
1	0.073375	-0.093110	0.073480	0.651479	-0.5172	0.984752	0.830215	0.323060	0.092907	-0.288653	...	0	0	0	1	0	0	0	0	1	0
2	0.073375	0.633618	0.375148	1.374795	-0.5172	0.951632	0.733308	1.364570	0.463568	-0.288653	...	0	0	0	1	0	0	0	0	1	0
3	-0.872563	0.224833	-0.043379	1.374795	-0.5172	1.084115	0.975575	0.456019	2.029558	-0.288653	...	0	0	0	1	0	0	0	0	1	0
4	-0.872563	-0.002269	0.068469	-0.795151	-0.5172	-0.207594	-0.962566	-0.574410	1.014077	-0.288653	...	0	0	0	1	0	0	0	0	1	0

5 rows × 276 columns

Xnew.columns

Index(['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond',
       'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2',
       ...
       'SaleType_ConLw', 'SaleType_New', 'SaleType_Oth', 'SaleType_WD',
       'SaleCondition_Abnorml', 'SaleCondition_AdjLand',
       'SaleCondition_Alloca', 'SaleCondition_Family', 'SaleCondition_Normal',
       'SaleCondition_Partial'],
      dtype='object', length=276)

from sklearn.model_selection import train_test_split

xtrain,xtest,ytrain,ytest=train_test_split(Xnew,Y,test_size=0.3,random_state=54)

xtrain.shape

(726, 276)

xtrain.columns

Index(['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond',
       'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2',
       ...
       'SaleType_ConLw', 'SaleType_New', 'SaleType_Oth', 'SaleType_WD',
       'SaleCondition_Abnorml', 'SaleCondition_AdjLand',
       'SaleCondition_Alloca', 'SaleCondition_Family', 'SaleCondition_Normal',
       'SaleCondition_Partial'],
      dtype='object', length=276)

ytrain.shape

(726,)

ytrain.info()

<class 'pandas.core.series.Series'>
Int64Index: 726 entries, 24 to 898
Series name: SalePrice
Non-Null Count  Dtype
--------------  -----
726 non-null    int64
dtypes: int64(1)
memory usage: 11.3 KB

from statsmodels.api import OLS,add_constant

Xconst=add_constant(xtrain)
ols=OLS(ytrain,Xconst)
model=ols.fit()

model.summary()

OLS Regression Results

Dep. Variable:	SalePrice	R-squared:	0.953
Model:	OLS	Adj. R-squared:	0.935
Method:	Least Squares	F-statistic:	52.82
Date:	Sat, 26 Aug 2023	Prob (F-statistic):	1.05e-259
Time:	11:22:09	Log-Likelihood:	-7964.7
No. Observations:	726	AIC:	1.633e+04
Df Residuals:	525	BIC:	1.725e+04
Df Model:	200
Covariance Type:	nonrobust

	coef	std err	t	P>|t|	[0.025	0.975]
MSSubClass	-1.315e+04	6547.419	-2.009	0.045	-2.6e+04	-290.031
LotFrontage	-260.4685	1314.618	-0.198	0.843	-2843.026	2322.089
LotArea	1.287e+04	3440.242	3.741	0.000	6110.629	1.96e+04
OverallQual	8283.9104	1678.468	4.935	0.000	4986.571	1.16e+04
OverallCond	5896.5633	1172.848	5.028	0.000	3592.512	8200.614
YearBuilt	9837.1789	3004.526	3.274	0.001	3934.810	1.57e+04
YearRemodAdd	1206.9199	1408.688	0.857	0.392	-1560.438	3974.277
MasVnrArea	1473.3009	1670.927	0.882	0.378	-1809.224	4755.825
BsmtFinSF1	6388.6517	1281.327	4.986	0.000	3871.493	8905.810
BsmtFinSF2	5896.6564	3122.203	1.889	0.059	-236.889	1.2e+04
BsmtUnfSF	-33.5562	1076.981	-0.031	0.975	-2149.277	2082.164
TotalBsmtSF	8776.4351	1717.973	5.109	0.000	5401.489	1.22e+04
1stFlrSF	4766.9636	1.26e+04	0.378	0.705	-2e+04	2.95e+04
2ndFlrSF	1.122e+04	1.38e+04	0.816	0.415	-1.58e+04	3.82e+04
GrLivArea	1.709e+04	1.7e+04	1.007	0.314	-1.63e+04	5.04e+04
BsmtFullBath	397.9571	1174.296	0.339	0.735	-1908.940	2704.854
BsmtHalfBath	-3884.5450	573.727	-6.771	0.000	-5011.627	-2757.463
FullBath	4312.3842	1546.375	2.789	0.005	1274.542	7350.226
HalfBath	1391.3756	1315.070	1.058	0.291	-1192.070	3974.821
BedroomAbvGr	-913.1800	1426.761	-0.640	0.522	-3716.042	1889.682
KitchenAbvGr	-3407.4397	503.261	-6.771	0.000	-4396.092	-2418.788
TotRmsAbvGrd	-1036.6683	1827.880	-0.567	0.571	-4627.526	2554.189
Fireplaces	3305.4939	1186.072	2.787	0.006	975.464	5635.523
GarageYrBlt	-262.5252	1552.134	-0.169	0.866	-3311.682	2786.631
GarageCars	2592.5982	1886.022	1.375	0.170	-1112.478	6297.675
GarageArea	3241.3391	1964.675	1.650	0.100	-618.251	7100.929
WoodDeckSF	4139.0441	939.410	4.406	0.000	2293.580	5984.508
OpenPorchSF	1364.6058	1117.353	1.221	0.223	-830.427	3559.638
EnclosedPorch	314.8829	1235.887	0.255	0.799	-2113.009	2742.775
3SsnPorch	-1.9e+04	2.3e+04	-0.827	0.409	-6.41e+04	2.61e+04
ScreenPorch	357.1218	1436.578	0.249	0.804	-2465.025	3179.268
MoSold	104.2274	756.613	0.138	0.890	-1382.133	1590.587
YrSold	140.5732	775.208	0.181	0.856	-1382.318	1663.464
MSZoning_C (all)	-1.973e+04	9540.011	-2.068	0.039	-3.85e+04	-986.224
MSZoning_FV	2.463e+04	6400.234	3.848	0.000	1.21e+04	3.72e+04
MSZoning_RH	-4637.8667	7896.246	-0.587	0.557	-2.01e+04	1.09e+04
MSZoning_RL	4407.9207	3951.065	1.116	0.265	-3353.917	1.22e+04
MSZoning_RM	1.145e+04	4259.858	2.687	0.007	3078.333	1.98e+04
Street_Grvl	1.687e+04	9491.563	1.777	0.076	-1777.977	3.55e+04
Street_Pave	-753.7680	8763.872	-0.086	0.931	-1.8e+04	1.65e+04
LotShape_IR1	5562.8497	3350.516	1.660	0.097	-1019.216	1.21e+04
LotShape_IR2	4869.2742	5034.150	0.967	0.334	-5020.277	1.48e+04
LotShape_IR3	-242.9744	8289.449	-0.029	0.977	-1.65e+04	1.6e+04
LotShape_Reg	5925.2134	3525.365	1.681	0.093	-1000.340	1.29e+04
LandContour_Bnk	7995.1203	4274.066	1.871	0.062	-401.252	1.64e+04
LandContour_HLS	1.781e+04	4086.966	4.358	0.000	9784.082	2.58e+04
LandContour_Low	-1.705e+04	5523.692	-3.086	0.002	-2.79e+04	-6195.030
LandContour_Lvl	7352.6291	2642.968	2.782	0.006	2160.536	1.25e+04
Utilities_AllPub	1.611e+04	2380.005	6.771	0.000	1.14e+04	2.08e+04
Utilities_NoSeWa	-1.609e-10	6e-11	-2.682	0.008	-2.79e-10	-4.3e-11
LotConfig_Corner	8365.4851	3655.809	2.288	0.023	1183.675	1.55e+04
LotConfig_CulDSac	1.133e+04	4135.856	2.739	0.006	3205.118	1.95e+04
LotConfig_FR2	-1040.4242	4513.464	-0.231	0.818	-9907.092	7826.243
LotConfig_FR3	-1.071e+04	1.27e+04	-0.845	0.398	-3.56e+04	1.42e+04
LotConfig_Inside	8166.3588	3455.492	2.363	0.018	1378.070	1.5e+04
LandSlope_Gtl	1.0049	6236.634	0.000	1.000	-1.23e+04	1.23e+04
LandSlope_Mod	-2274.7517	6781.438	-0.335	0.737	-1.56e+04	1.1e+04
LandSlope_Sev	1.839e+04	1.26e+04	1.454	0.146	-6448.529	4.32e+04
Neighborhood_Blmngtn	2.253e+04	7115.645	3.166	0.002	8549.491	3.65e+04
Neighborhood_Blueste	-6630.9441	1.79e+04	-0.370	0.712	-4.18e+04	2.86e+04
Neighborhood_BrDale	2551.2707	9232.445	0.276	0.782	-1.56e+04	2.07e+04
Neighborhood_BrkSide	-2815.8276	5833.370	-0.483	0.630	-1.43e+04	8643.786
Neighborhood_ClearCr	3044.3355	7729.911	0.394	0.694	-1.21e+04	1.82e+04
Neighborhood_CollgCr	-4170.0792	3149.905	-1.324	0.186	-1.04e+04	2017.887
Neighborhood_Crawfor	2.082e+04	6436.559	3.234	0.001	8172.909	3.35e+04
Neighborhood_Edwards	-9957.5330	3757.448	-2.650	0.008	-1.73e+04	-2576.053
Neighborhood_Gilbert	-6617.4477	4085.241	-1.620	0.106	-1.46e+04	1407.980
Neighborhood_IDOTRR	-8494.0362	7401.705	-1.148	0.252	-2.3e+04	6046.561
Neighborhood_MeadowV	-1.155e+04	8766.009	-1.318	0.188	-2.88e+04	5668.167
Neighborhood_Mitchel	-8888.8204	4481.737	-1.983	0.048	-1.77e+04	-84.481
Neighborhood_NAmes	-8133.5656	3157.561	-2.576	0.010	-1.43e+04	-1930.559
Neighborhood_NPkVill	1.612e+04	1.15e+04	1.401	0.162	-6483.589	3.87e+04
Neighborhood_NWAmes	-1.505e+04	4147.915	-3.628	0.000	-2.32e+04	-6900.090
Neighborhood_NoRidge	1.219e+04	5690.954	2.141	0.033	1005.287	2.34e+04
Neighborhood_NridgHt	1.388e+04	4827.917	2.874	0.004	4392.860	2.34e+04
Neighborhood_OldTown	-1.704e+04	5502.935	-3.097	0.002	-2.79e+04	-6234.508
Neighborhood_SWISU	-1.057e+04	8678.849	-1.218	0.224	-2.76e+04	6480.937
Neighborhood_Sawyer	-6521.8028	3885.752	-1.678	0.094	-1.42e+04	1111.730
Neighborhood_SawyerW	-5028.4263	4413.136	-1.139	0.255	-1.37e+04	3641.148
Neighborhood_Somerst	-7246.4231	6273.337	-1.155	0.249	-1.96e+04	5077.502
Neighborhood_StoneBr	6.023e+04	6716.782	8.967	0.000	4.7e+04	7.34e+04
Neighborhood_Timber	-7879.7337	5016.435	-1.571	0.117	-1.77e+04	1975.016
Neighborhood_Veenker	1354.9000	1.43e+04	0.095	0.924	-2.67e+04	2.94e+04
Condition1_Artery	-9465.7000	5654.475	-1.674	0.095	-2.06e+04	1642.475
Condition1_Feedr	-23.5864	4896.137	-0.005	0.996	-9642.012	9594.839
Condition1_Norm	3728.5999	3618.612	1.030	0.303	-3380.138	1.08e+04
Condition1_PosA	1.414e+04	1.26e+04	1.121	0.263	-1.06e+04	3.89e+04
Condition1_PosN	9318.8611	9672.546	0.963	0.336	-9682.786	2.83e+04
Condition1_RRAe	-1.35e+04	8214.265	-1.643	0.101	-2.96e+04	2640.356
Condition1_RRAn	-85.4550	5575.391	-0.015	0.988	-1.1e+04	1.09e+04
Condition1_RRNe	-2254.3990	1.59e+04	-0.142	0.887	-3.35e+04	2.9e+04
Condition1_RRNn	1.425e+04	1.46e+04	0.977	0.329	-1.44e+04	4.29e+04
Condition2_Artery	1.231e-12	2.61e-11	0.047	0.962	-5.01e-11	5.26e-11
Condition2_Feedr	5064.5926	8192.456	0.618	0.537	-1.1e+04	2.12e+04
Condition2_Norm	1.105e+04	7675.804	1.440	0.151	-4029.291	2.61e+04
Condition2_PosA	2.996e-11	3.19e-11	0.939	0.348	-3.27e-11	9.26e-11
Condition2_PosN	4.697e-11	2.92e-11	1.610	0.108	-1.03e-11	1.04e-10
Condition2_RRAe	-6.856e-11	3.87e-11	-1.771	0.077	-1.45e-10	7.49e-12
Condition2_RRAn	-8.321e-11	3.52e-11	-2.366	0.018	-1.52e-10	-1.41e-11
Condition2_RRNn	-1.806e-11	2.52e-11	-0.716	0.474	-6.76e-11	3.15e-11
BldgType_1Fam	7115.4212	8702.688	0.818	0.414	-9980.947	2.42e+04
BldgType_2fmCon	3026.8797	2.22e+04	0.136	0.892	-4.06e+04	4.67e+04
BldgType_Duplex	-1.296e+04	1.21e+04	-1.070	0.285	-3.67e+04	1.08e+04
BldgType_Twnhs	7282.8735	1.12e+04	0.649	0.516	-1.47e+04	2.93e+04
BldgType_TwnhsE	1.165e+04	1.07e+04	1.091	0.276	-9327.572	3.26e+04
HouseStyle_1.5Fin	-1694.3085	4813.348	-0.352	0.725	-1.12e+04	7761.479
HouseStyle_1.5Unf	7281.7269	8259.900	0.882	0.378	-8944.788	2.35e+04
HouseStyle_1Story	-1555.9546	7211.536	-0.216	0.829	-1.57e+04	1.26e+04
HouseStyle_2.5Fin	-1.623e+04	1.77e+04	-0.915	0.361	-5.11e+04	1.86e+04
HouseStyle_2.5Unf	2709.1949	1.79e+04	0.151	0.880	-3.25e+04	3.79e+04
HouseStyle_2Story	1073.1293	4785.761	0.224	0.823	-8328.464	1.05e+04
HouseStyle_SFoyer	1.488e+04	7677.669	1.938	0.053	-207.154	3e+04
HouseStyle_SLvl	9658.0611	6397.015	1.510	0.132	-2908.828	2.22e+04
RoofStyle_Flat	-4160.3395	8628.687	-0.482	0.630	-2.11e+04	1.28e+04
RoofStyle_Gable	-1934.3417	5113.683	-0.378	0.705	-1.2e+04	8111.452
RoofStyle_Gambrel	7143.8783	9375.612	0.762	0.446	-1.13e+04	2.56e+04
RoofStyle_Hip	1134.1691	5272.631	0.215	0.830	-9223.877	1.15e+04
RoofStyle_Mansard	1.393e+04	1.09e+04	1.280	0.201	-7454.425	3.53e+04
RoofStyle_Shed	8.508e-12	1.93e-11	0.440	0.660	-2.95e-11	4.65e-11
RoofMatl_ClyTile	-1.77e-12	2.31e-11	-0.077	0.939	-4.71e-11	4.36e-11
RoofMatl_CompShg	2.619e+04	1.14e+04	2.299	0.022	3806.595	4.86e+04
RoofMatl_Membran	-2.037e-11	2.15e-11	-0.947	0.344	-6.26e-11	2.19e-11
RoofMatl_Metal	3.193e-11	2.53e-11	1.264	0.207	-1.77e-11	8.16e-11
RoofMatl_Roll	9.673e-12	2.42e-11	0.400	0.690	-3.79e-11	5.72e-11
RoofMatl_Tar&Grv	-4160.3395	8628.687	-0.482	0.630	-2.11e+04	1.28e+04
RoofMatl_WdShake	-5920.0443	1.59e+04	-0.372	0.710	-3.72e+04	2.54e+04
RoofMatl_WdShngl	2.498e-11	2.41e-11	1.037	0.300	-2.23e-11	7.23e-11
Exterior1st_AsbShng	2.129e+04	1.67e+04	1.273	0.204	-1.16e+04	5.41e+04
Exterior1st_AsphShn	2.522e-12	2.05e-11	0.123	0.902	-3.77e-11	4.27e-11
Exterior1st_BrkComm	-1807.4964	2.49e+04	-0.073	0.942	-5.06e+04	4.7e+04
Exterior1st_BrkFace	1.658e+04	7458.438	2.224	0.027	1932.197	3.12e+04
Exterior1st_CBlock	1.7e+04	1.18e+04	1.437	0.151	-6241.513	4.02e+04
Exterior1st_CemntBd	4775.5565	2889.020	1.653	0.099	-899.902	1.05e+04
Exterior1st_HdBoard	1167.4066	6205.911	0.188	0.851	-1.1e+04	1.34e+04
Exterior1st_ImStucc	8.55e-12	1.35e-11	0.631	0.528	-1.81e-11	3.52e-11
Exterior1st_MetalSd	3080.1012	1.03e+04	0.300	0.764	-1.71e+04	2.32e+04
Exterior1st_Plywood	6258.6768	6368.168	0.983	0.326	-6251.544	1.88e+04
Exterior1st_Stone	1.25e-11	1.42e-11	0.882	0.378	-1.53e-11	4.03e-11
Exterior1st_Stucco	-5.726e+04	2.2e+04	-2.605	0.009	-1e+05	-1.41e+04
Exterior1st_VinylSd	-1437.8909	8293.849	-0.173	0.862	-1.77e+04	1.49e+04
Exterior1st_Wd Sdng	-1609.8616	5957.645	-0.270	0.787	-1.33e+04	1.01e+04
Exterior1st_WdShing	8077.2753	7596.801	1.063	0.288	-6846.587	2.3e+04
Exterior2nd_AsbShng	-5201.2122	1.56e+04	-0.333	0.739	-3.59e+04	2.55e+04
Exterior2nd_AsphShn	-7.205e-13	1.72e-11	-0.042	0.967	-3.44e-11	3.3e-11
Exterior2nd_Brk Cmn	-7196.0535	1.39e+04	-0.518	0.605	-3.45e+04	2.01e+04
Exterior2nd_BrkFace	-2.015e+04	9836.586	-2.048	0.041	-3.95e+04	-823.243
Exterior2nd_CBlock	1.7e+04	1.18e+04	1.437	0.151	-6241.513	4.02e+04
Exterior2nd_CmentBd	4775.5565	2889.020	1.653	0.099	-899.902	1.05e+04
Exterior2nd_HdBoard	-5843.1708	5825.026	-1.003	0.316	-1.73e+04	5600.052
Exterior2nd_ImStucc	-2.62e+04	1.19e+04	-2.195	0.029	-4.96e+04	-2746.387
Exterior2nd_MetalSd	4151.3490	1.01e+04	0.411	0.682	-1.57e+04	2.4e+04
Exterior2nd_Other	-1.141e-11	8.83e-12	-1.293	0.197	-2.88e-11	5.93e-12
Exterior2nd_Plywood	-8516.1279	5095.901	-1.671	0.095	-1.85e+04	1494.733
Exterior2nd_Stone	-1.007e-11	1.14e-11	-0.884	0.377	-3.24e-11	1.23e-11
Exterior2nd_Stucco	6.631e+04	2.13e+04	3.115	0.002	2.45e+04	1.08e+05
Exterior2nd_VinylSd	1599.1743	8152.917	0.196	0.845	-1.44e+04	1.76e+04
Exterior2nd_Wd Sdng	4225.9916	5722.357	0.739	0.461	-7015.539	1.55e+04
Exterior2nd_Wd Shng	-8847.1711	6991.072	-1.265	0.206	-2.26e+04	4886.739
MasVnrType_BrkCmn	-2036.8548	7386.709	-0.276	0.783	-1.65e+04	1.25e+04
MasVnrType_BrkFace	2150.7161	2787.011	0.772	0.441	-3324.346	7625.779
MasVnrType_None	1705.0586	3063.321	0.557	0.578	-4312.813	7722.930
MasVnrType_Stone	1.43e+04	3308.295	4.321	0.000	7796.321	2.08e+04
ExterQual_Ex	1.619e+04	6489.369	2.495	0.013	3443.391	2.89e+04
ExterQual_Fa	-4122.5778	1.16e+04	-0.354	0.723	-2.7e+04	1.87e+04
ExterQual_Gd	1893.6648	4652.527	0.407	0.684	-7246.191	1.1e+04
ExterQual_TA	2151.5659	4654.361	0.462	0.644	-6991.894	1.13e+04
ExterCond_Ex	2.641e-12	8.23e-12	0.321	0.748	-1.35e-11	1.88e-11
ExterCond_Fa	4131.7192	5543.503	0.745	0.456	-6758.452	1.5e+04
ExterCond_Gd	4368.9766	3332.772	1.311	0.190	-2178.229	1.09e+04
ExterCond_Po	4.715e-12	1.1e-11	0.430	0.667	-1.68e-11	2.63e-11
ExterCond_TA	7613.6670	2911.494	2.615	0.009	1894.057	1.33e+04
Foundation_BrkTil	-6597.1028	5249.409	-1.257	0.209	-1.69e+04	3715.323
Foundation_CBlock	-390.9368	4583.020	-0.085	0.932	-9394.246	8612.372
Foundation_PConc	306.7008	4702.587	0.065	0.948	-8931.498	9544.899
Foundation_Slab	3338.3849	8075.445	0.413	0.679	-1.25e+04	1.92e+04
Foundation_Stone	1.014e+04	1.27e+04	0.800	0.424	-1.47e+04	3.5e+04
Foundation_Wood	9318.1816	1.73e+04	0.539	0.590	-2.47e+04	4.33e+04
BsmtQual_Ex	9907.7574	3547.356	2.793	0.005	2939.002	1.69e+04
BsmtQual_Fa	1.018e+04	4531.121	2.246	0.025	1277.498	1.91e+04
BsmtQual_Gd	-3212.5523	2376.286	-1.352	0.177	-7880.748	1455.644
BsmtQual_TA	-759.6953	2383.564	-0.319	0.750	-5442.191	3922.800
BsmtCond_Fa	383.9748	3881.280	0.099	0.921	-7240.772	8008.722
BsmtCond_Gd	5359.8455	3239.165	1.655	0.099	-1003.471	1.17e+04
BsmtCond_Po	-1.442e-11	7.31e-12	-1.974	0.049	-2.88e-11	-7e-14
BsmtCond_TA	1.037e+04	2368.752	4.378	0.000	5717.147	1.5e+04
BsmtExposure_Av	1522.1174	1830.707	0.831	0.406	-2074.293	5118.527
BsmtExposure_Gd	1.595e+04	2774.556	5.748	0.000	1.05e+04	2.14e+04
BsmtExposure_Mn	-1265.4973	2423.751	-0.522	0.602	-6026.938	3495.943
BsmtExposure_No	-90.3296	1655.409	-0.055	0.957	-3342.368	3161.709
BsmtFinType1_ALQ	4633.0114	2010.302	2.305	0.022	683.787	8582.236
BsmtFinType1_BLQ	-1012.8177	2361.228	-0.429	0.668	-5651.433	3625.798
BsmtFinType1_GLQ	1.22e+04	2134.104	5.715	0.000	8005.000	1.64e+04
BsmtFinType1_LwQ	-3944.2561	3366.343	-1.172	0.242	-1.06e+04	2668.901
BsmtFinType1_Rec	1816.3413	2575.133	0.705	0.481	-3242.488	6875.171
BsmtFinType1_Unf	2424.6528	2193.495	1.105	0.269	-1884.453	6733.758
BsmtFinType2_ALQ	1.557e+04	1.33e+04	1.173	0.241	-1.05e+04	4.16e+04
BsmtFinType2_BLQ	-1.019e+04	5873.208	-1.736	0.083	-2.17e+04	1343.232
BsmtFinType2_GLQ	1.653e+04	9778.485	1.690	0.092	-2684.132	3.57e+04
BsmtFinType2_LwQ	-4642.6081	4903.219	-0.947	0.344	-1.43e+04	4989.730
BsmtFinType2_Rec	-7823.9010	5531.896	-1.414	0.158	-1.87e+04	3043.469
BsmtFinType2_Unf	6677.0985	6092.770	1.096	0.274	-5292.105	1.86e+04
Heating_Floor	0	0	nan	nan	0	0
Heating_GasA	-6587.0482	8170.722	-0.806	0.421	-2.26e+04	9464.277
Heating_GasW	-1.335e+04	1.15e+04	-1.163	0.245	-3.59e+04	9201.280
Heating_Grav	1.403e+04	1.4e+04	1.002	0.317	-1.35e+04	4.15e+04
Heating_OthW	0	0	nan	nan	0	0
Heating_Wall	2.202e+04	1.67e+04	1.316	0.189	-1.09e+04	5.49e+04
HeatingQC_Ex	3958.7241	2018.545	1.961	0.050	-6.693	7924.141
HeatingQC_Fa	3183.5497	4013.857	0.793	0.428	-4701.644	1.11e+04
HeatingQC_Gd	4388.2889	2102.819	2.087	0.037	257.316	8519.262
HeatingQC_Po	0	0	nan	nan	0	0
HeatingQC_TA	4583.8002	1906.134	2.405	0.017	839.214	8328.386
CentralAir_N	9107.5930	2761.543	3.298	0.001	3682.562	1.45e+04
CentralAir_Y	7006.7699	2846.041	2.462	0.014	1415.743	1.26e+04
Electrical_FuseA	8272.3678	6295.764	1.314	0.189	-4095.615	2.06e+04
Electrical_FuseF	953.3996	7169.303	0.133	0.894	-1.31e+04	1.5e+04
Electrical_FuseP	1879.5518	1.73e+04	0.109	0.914	-3.21e+04	3.59e+04
Electrical_Mix	0	0	nan	nan	0	0
Electrical_SBrkr	5009.0436	6224.558	0.805	0.421	-7219.056	1.72e+04
KitchenQual_Ex	1.796e+04	3521.980	5.100	0.000	1.1e+04	2.49e+04
KitchenQual_Fa	272.7772	4734.317	0.058	0.954	-9027.755	9573.309
KitchenQual_Gd	-1020.6401	2320.748	-0.440	0.660	-5579.733	3538.453
KitchenQual_TA	-1101.2641	2187.552	-0.503	0.615	-5398.694	3196.166
Functional_Maj1	-5909.9604	1.1e+04	-0.536	0.592	-2.76e+04	1.58e+04
Functional_Maj2	7311.6884	2.09e+04	0.351	0.726	-3.37e+04	4.83e+04
Functional_Min1	2773.6904	6744.581	0.411	0.681	-1.05e+04	1.6e+04
Functional_Min2	4538.7546	6658.493	0.682	0.496	-8541.807	1.76e+04
Functional_Mod	-9981.1420	9461.133	-1.055	0.292	-2.86e+04	8605.187
Functional_Sev	0	0	nan	nan	0	0
Functional_Typ	1.738e+04	5017.224	3.464	0.001	7525.031	2.72e+04
FireplaceQu_Ex	-1.101e+04	7102.582	-1.550	0.122	-2.5e+04	2942.254
FireplaceQu_Fa	-2359.1252	4643.678	-0.508	0.612	-1.15e+04	6763.347
FireplaceQu_Gd	6794.2601	2725.762	2.493	0.013	1439.520	1.21e+04
FireplaceQu_Po	1.938e+04	5454.613	3.553	0.000	8661.996	3.01e+04
FireplaceQu_TA	3312.3996	2772.225	1.195	0.233	-2133.616	8758.415
GarageType_2Types	0	0	nan	nan	0	0
GarageType_Attchd	-4931.1936	4200.811	-1.174	0.241	-1.32e+04	3321.269
GarageType_Basment	-2229.4607	6763.426	-0.330	0.742	-1.55e+04	1.11e+04
GarageType_BuiltIn	-2776.8124	5091.721	-0.545	0.586	-1.28e+04	7225.836
GarageType_CarPort	2.801e+04	1.49e+04	1.880	0.061	-1251.668	5.73e+04
GarageType_Detchd	-1957.7472	4342.540	-0.451	0.652	-1.05e+04	6573.141
GarageFinish_Fin	3640.9994	1548.627	2.351	0.019	598.733	6683.266
GarageFinish_RFn	6731.2640	1477.132	4.557	0.000	3829.450	9633.079
GarageFinish_Unf	5742.0994	1736.538	3.307	0.001	2330.683	9153.516
GarageQual_Ex	7127.8693	6099.030	1.169	0.243	-4853.632	1.91e+04
GarageQual_Fa	2101.8684	5578.248	0.377	0.706	-8856.561	1.31e+04
GarageQual_Gd	609.3395	7658.405	0.080	0.937	-1.44e+04	1.57e+04
GarageQual_Po	0	0	nan	nan	0	0
GarageQual_TA	6275.2856	4051.434	1.549	0.122	-1683.728	1.42e+04
GarageCond_Ex	7127.8693	6099.030	1.169	0.243	-4853.632	1.91e+04
GarageCond_Fa	-4718.4580	8188.980	-0.576	0.565	-2.08e+04	1.14e+04
GarageCond_Gd	1.587e+04	1.4e+04	1.135	0.257	-1.16e+04	4.34e+04
GarageCond_Po	-3215.0442	2.16e+04	-0.149	0.882	-4.56e+04	3.92e+04
GarageCond_TA	1048.3214	7475.140	0.140	0.889	-1.36e+04	1.57e+04
PavedDrive_N	9346.5553	3438.231	2.718	0.007	2592.175	1.61e+04
PavedDrive_P	-953.1221	4757.017	-0.200	0.841	-1.03e+04	8392.003
PavedDrive_Y	7720.9297	3106.094	2.486	0.013	1619.030	1.38e+04
Fence_GdPrv	5947.7226	4219.920	1.409	0.159	-2342.279	1.42e+04
Fence_GdWo	9617.4168	3709.081	2.593	0.010	2330.954	1.69e+04
Fence_MnPrv	7229.1092	2717.115	2.661	0.008	1891.357	1.26e+04
Fence_MnWw	-6679.8858	6636.636	-1.007	0.315	-1.97e+04	6357.738
SaleType_COD	-1618.4116	6526.789	-0.248	0.804	-1.44e+04	1.12e+04
SaleType_CWD	5.253e+04	1.26e+04	4.168	0.000	2.78e+04	7.73e+04
SaleType_Con	5743.7135	1.63e+04	0.353	0.724	-2.62e+04	3.77e+04
SaleType_ConLD	-7598.1329	1.07e+04	-0.711	0.477	-2.86e+04	1.34e+04
SaleType_ConLI	-4069.6281	1.81e+04	-0.225	0.822	-3.96e+04	3.15e+04
SaleType_ConLw	-9888.1004	1.32e+04	-0.748	0.455	-3.59e+04	1.61e+04
SaleType_New	-2.665e+04	1.3e+04	-2.049	0.041	-5.22e+04	-1102.959
SaleType_Oth	6907.1675	1.21e+04	0.571	0.568	-1.69e+04	3.07e+04
SaleType_WD	763.1038	4724.799	0.162	0.872	-8518.729	1e+04
SaleCondition_Abnorml	-5180.2760	5587.306	-0.927	0.354	-1.62e+04	5795.946
SaleCondition_AdjLand	2.61e+04	1.79e+04	1.460	0.145	-9017.280	6.12e+04
SaleCondition_Alloca	-4.918e+04	1.71e+04	-2.877	0.004	-8.28e+04	-1.56e+04
SaleCondition_Family	8611.0939	7932.263	1.086	0.278	-6971.780	2.42e+04
SaleCondition_Normal	107.0441	5400.131	0.020	0.984	-1.05e+04	1.07e+04
SaleCondition_Partial	3.566e+04	1.3e+04	2.734	0.006	1e+04	6.13e+04

Omnibus:	136.128	Durbin-Watson:	1.911
Prob(Omnibus):	0.000	Jarque-Bera (JB):	2761.370
Skew:	0.089	Prob(JB):	0.00
Kurtosis:	12.553	Cond. No.	1.25e+16

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.
[2] The smallest eigenvalue is 1.27e-28. This might indicate that there are
strong multicollinearity problems or that the design matrix is singular.

rsq=round(model.rsquared_adj,4)

rsq

0.9346

model.pvalues.sort_values()

Neighborhood_StoneBr    5.352529e-18
KitchenAbvGr            3.439900e-11
Utilities_AllPub        3.439900e-11
BsmtHalfBath            3.439900e-11
BsmtExposure_Gd         1.533103e-08
                            ...     
HeatingQC_Po                     NaN
Electrical_Mix                   NaN
Functional_Sev                   NaN
GarageType_2Types                NaN
GarageQual_Po                    NaN
Length: 276, dtype: float64

col_to_drop=model.pvalues.sort_values().index[-1]

Xnew=Xnew.drop(labels=col_to_drop,axis=1)
xtrain,xtest,ytrain,ytest=train_test_split(Xnew,Y,test_size=0.2,random_state=54)
Xconst=add_constant(xtrain)
ols=OLS(ytrain,Xconst)
model=ols.fit()
rsq=round(model.rsquared_adj,4)
col_to_drop=model.pvalues.sort_values().index[-1]
print('Adjusted r_squared',rsq)

print('column to drop',col_to_drop)

Adjusted r_squared 0.9406
column to drop Exterior2nd_CBlock

Xnew.columns

Index(['MSSubClass', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt',
       'BsmtFinSF1', 'BsmtFinSF2', 'TotalBsmtSF', '2ndFlrSF', 'GrLivArea',
       ...
       'GarageQual_TA', 'GarageCond_Fa', 'GarageCond_Gd', 'Fence_GdPrv',
       'Fence_GdWo', 'Fence_MnPrv', 'SaleType_CWD', 'SaleCondition_Abnorml',
       'SaleCondition_Alloca', 'SaleCondition_Partial'],
      dtype='object', length=113)

Xnew.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1038 entries, 0 to 1037
Columns: 113 entries, MSSubClass to SaleCondition_Partial
dtypes: float64(18), uint8(95)
memory usage: 242.4 KB

Xnew=Xnew.drop(labels=['Exterior1st_ImStucc','HouseStyle_2.5Fin'],axis=1)

Xnew.columns

Index(['MSSubClass', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt',
       'BsmtFinSF1', 'BsmtFinSF2', 'TotalBsmtSF', '2ndFlrSF', 'GrLivArea',
       ...
       'GarageQual_TA', 'GarageCond_Fa', 'GarageCond_Gd', 'Fence_GdPrv',
       'Fence_GdWo', 'Fence_MnPrv', 'SaleType_CWD', 'SaleCondition_Abnorml',
       'SaleCondition_Alloca', 'SaleCondition_Partial'],
      dtype='object', length=111)

Applying linear regression

from sklearn.linear_model import LinearRegression

lm=LinearRegression()

model=lm.fit(xtrain,ytrain)

tr_pred=model.predict(xtrain)

ts_pred=model.predict(xtest)

from sklearn.metrics import mean_squared_error,mean_absolute_error

tr_err=mean_squared_error(ytrain,tr_pred)
ts_err=mean_squared_error(ytest,ts_pred)
tr_ab=mean_absolute_error(ytrain,tr_pred)
ts_ab=mean_absolute_error(ytest,ts_pred)

print('train_err',tr_err)
print('test_err',ts_err)
print('train_ab',tr_ab)
print('test_ab',ts_ab)

train_err 228448012.2987952
test_err 4.273445948639171e+26
train_ab 10649.578313253012
test_ab 1433367142066.1394

len('Xtrain')

6

Model is Overfit (Training Error is less than Testing Error)

from sklearn.linear_model import Ridge

rr=Ridge(alpha=23)
model=rr.fit(xtrain,ytrain)
tr_pred=model.predict(xtrain)
ts_pred=model.predict(xtest)

tr_err=mean_squared_error(ytrain,tr_pred)
ts_err=mean_squared_error(ytest,ts_pred)
tr_ab=mean_absolute_error(ytrain,tr_pred)
ts_ab=mean_absolute_error(ytest,ts_pred)

print('train_err',tr_err)
print('test_err',ts_err)
print('train_ab',tr_ab)
print('test_ab',ts_ab)

train_err 306758235.53366125
test_err 453827951.64898556
train_ab 12176.956117645803
test_ab 14044.049405250164

w=[]
e=0.01
for i in range(0,1000,1):
    w.append(e)
    e=round(e+0.01,2)

w

[0.01,
 0.02,
 0.03,
 0.04,
 0.05,
 0.06,
 0.07,
 0.08,
 0.09,
 0.1,
 0.11,
 0.12,
 0.13,
 0.14,
 0.15,
 0.16,
 0.17,
 0.18,
 0.19,
 0.2,
 0.21,
 0.22,
 0.23,
 0.24,
 0.25,
 0.26,
 0.27,
 0.28,
 0.29,
 0.3,
 0.31,
 0.32,
 0.33,
 0.34,
 0.35,
 0.36,
 0.37,
 0.38,
 0.39,
 0.4,
 0.41,
 0.42,
 0.43,
 0.44,
 0.45,
 0.46,
 0.47,
 0.48,
 0.49,
 0.5,
 0.51,
 0.52,
 0.53,
 0.54,
 0.55,
 0.56,
 0.57,
 0.58,
 0.59,
 0.6,
 0.61,
 0.62,
 0.63,
 0.64,
 0.65,
 0.66,
 0.67,
 0.68,
 0.69,
 0.7,
 0.71,
 0.72,
 0.73,
 0.74,
 0.75,
 0.76,
 0.77,
 0.78,
 0.79,
 0.8,
 0.81,
 0.82,
 0.83,
 0.84,
 0.85,
 0.86,
 0.87,
 0.88,
 0.89,
 0.9,
 0.91,
 0.92,
 0.93,
 0.94,
 0.95,
 0.96,
 0.97,
 0.98,
 0.99,
 1.0,
 1.01,
 1.02,
 1.03,
 1.04,
 1.05,
 1.06,
 1.07,
 1.08,
 1.09,
 1.1,
 1.11,
 1.12,
 1.13,
 1.14,
 1.15,
 1.16,
 1.17,
 1.18,
 1.19,
 1.2,
 1.21,
 1.22,
 1.23,
 1.24,
 1.25,
 1.26,
 1.27,
 1.28,
 1.29,
 1.3,
 1.31,
 1.32,
 1.33,
 1.34,
 1.35,
 1.36,
 1.37,
 1.38,
 1.39,
 1.4,
 1.41,
 1.42,
 1.43,
 1.44,
 1.45,
 1.46,
 1.47,
 1.48,
 1.49,
 1.5,
 1.51,
 1.52,
 1.53,
 1.54,
 1.55,
 1.56,
 1.57,
 1.58,
 1.59,
 1.6,
 1.61,
 1.62,
 1.63,
 1.64,
 1.65,
 1.66,
 1.67,
 1.68,
 1.69,
 1.7,
 1.71,
 1.72,
 1.73,
 1.74,
 1.75,
 1.76,
 1.77,
 1.78,
 1.79,
 1.8,
 1.81,
 1.82,
 1.83,
 1.84,
 1.85,
 1.86,
 1.87,
 1.88,
 1.89,
 1.9,
 1.91,
 1.92,
 1.93,
 1.94,
 1.95,
 1.96,
 1.97,
 1.98,
 1.99,
 2.0,
 2.01,
 2.02,
 2.03,
 2.04,
 2.05,
 2.06,
 2.07,
 2.08,
 2.09,
 2.1,
 2.11,
 2.12,
 2.13,
 2.14,
 2.15,
 2.16,
 2.17,
 2.18,
 2.19,
 2.2,
 2.21,
 2.22,
 2.23,
 2.24,
 2.25,
 2.26,
 2.27,
 2.28,
 2.29,
 2.3,
 2.31,
 2.32,
 2.33,
 2.34,
 2.35,
 2.36,
 2.37,
 2.38,
 2.39,
 2.4,
 2.41,
 2.42,
 2.43,
 2.44,
 2.45,
 2.46,
 2.47,
 2.48,
 2.49,
 2.5,
 2.51,
 2.52,
 2.53,
 2.54,
 2.55,
 2.56,
 2.57,
 2.58,
 2.59,
 2.6,
 2.61,
 2.62,
 2.63,
 2.64,
 2.65,
 2.66,
 2.67,
 2.68,
 2.69,
 2.7,
 2.71,
 2.72,
 2.73,
 2.74,
 2.75,
 2.76,
 2.77,
 2.78,
 2.79,
 2.8,
 2.81,
 2.82,
 2.83,
 2.84,
 2.85,
 2.86,
 2.87,
 2.88,
 2.89,
 2.9,
 2.91,
 2.92,
 2.93,
 2.94,
 2.95,
 2.96,
 2.97,
 2.98,
 2.99,
 3.0,
 3.01,
 3.02,
 3.03,
 3.04,
 3.05,
 3.06,
 3.07,
 3.08,
 3.09,
 3.1,
 3.11,
 3.12,
 3.13,
 3.14,
 3.15,
 3.16,
 3.17,
 3.18,
 3.19,
 3.2,
 3.21,
 3.22,
 3.23,
 3.24,
 3.25,
 3.26,
 3.27,
 3.28,
 3.29,
 3.3,
 3.31,
 3.32,
 3.33,
 3.34,
 3.35,
 3.36,
 3.37,
 3.38,
 3.39,
 3.4,
 3.41,
 3.42,
 3.43,
 3.44,
 3.45,
 3.46,
 3.47,
 3.48,
 3.49,
 3.5,
 3.51,
 3.52,
 3.53,
 3.54,
 3.55,
 3.56,
 3.57,
 3.58,
 3.59,
 3.6,
 3.61,
 3.62,
 3.63,
 3.64,
 3.65,
 3.66,
 3.67,
 3.68,
 3.69,
 3.7,
 3.71,
 3.72,
 3.73,
 3.74,
 3.75,
 3.76,
 3.77,
 3.78,
 3.79,
 3.8,
 3.81,
 3.82,
 3.83,
 3.84,
 3.85,
 3.86,
 3.87,
 3.88,
 3.89,
 3.9,
 3.91,
 3.92,
 3.93,
 3.94,
 3.95,
 3.96,
 3.97,
 3.98,
 3.99,
 4.0,
 4.01,
 4.02,
 4.03,
 4.04,
 4.05,
 4.06,
 4.07,
 4.08,
 4.09,
 4.1,
 4.11,
 4.12,
 4.13,
 4.14,
 4.15,
 4.16,
 4.17,
 4.18,
 4.19,
 4.2,
 4.21,
 4.22,
 4.23,
 4.24,
 4.25,
 4.26,
 4.27,
 4.28,
 4.29,
 4.3,
 4.31,
 4.32,
 4.33,
 4.34,
 4.35,
 4.36,
 4.37,
 4.38,
 4.39,
 4.4,
 4.41,
 4.42,
 4.43,
 4.44,
 4.45,
 4.46,
 4.47,
 4.48,
 4.49,
 4.5,
 4.51,
 4.52,
 4.53,
 4.54,
 4.55,
 4.56,
 4.57,
 4.58,
 4.59,
 4.6,
 4.61,
 4.62,
 4.63,
 4.64,
 4.65,
 4.66,
 4.67,
 4.68,
 4.69,
 4.7,
 4.71,
 4.72,
 4.73,
 4.74,
 4.75,
 4.76,
 4.77,
 4.78,
 4.79,
 4.8,
 4.81,
 4.82,
 4.83,
 4.84,
 4.85,
 4.86,
 4.87,
 4.88,
 4.89,
 4.9,
 4.91,
 4.92,
 4.93,
 4.94,
 4.95,
 4.96,
 4.97,
 4.98,
 4.99,
 5.0,
 5.01,
 5.02,
 5.03,
 5.04,
 5.05,
 5.06,
 5.07,
 5.08,
 5.09,
 5.1,
 5.11,
 5.12,
 5.13,
 5.14,
 5.15,
 5.16,
 5.17,
 5.18,
 5.19,
 5.2,
 5.21,
 5.22,
 5.23,
 5.24,
 5.25,
 5.26,
 5.27,
 5.28,
 5.29,
 5.3,
 5.31,
 5.32,
 5.33,
 5.34,
 5.35,
 5.36,
 5.37,
 5.38,
 5.39,
 5.4,
 5.41,
 5.42,
 5.43,
 5.44,
 5.45,
 5.46,
 5.47,
 5.48,
 5.49,
 5.5,
 5.51,
 5.52,
 5.53,
 5.54,
 5.55,
 5.56,
 5.57,
 5.58,
 5.59,
 5.6,
 5.61,
 5.62,
 5.63,
 5.64,
 5.65,
 5.66,
 5.67,
 5.68,
 5.69,
 5.7,
 5.71,
 5.72,
 5.73,
 5.74,
 5.75,
 5.76,
 5.77,
 5.78,
 5.79,
 5.8,
 5.81,
 5.82,
 5.83,
 5.84,
 5.85,
 5.86,
 5.87,
 5.88,
 5.89,
 5.9,
 5.91,
 5.92,
 5.93,
 5.94,
 5.95,
 5.96,
 5.97,
 5.98,
 5.99,
 6.0,
 6.01,
 6.02,
 6.03,
 6.04,
 6.05,
 6.06,
 6.07,
 6.08,
 6.09,
 6.1,
 6.11,
 6.12,
 6.13,
 6.14,
 6.15,
 6.16,
 6.17,
 6.18,
 6.19,
 6.2,
 6.21,
 6.22,
 6.23,
 6.24,
 6.25,
 6.26,
 6.27,
 6.28,
 6.29,
 6.3,
 6.31,
 6.32,
 6.33,
 6.34,
 6.35,
 6.36,
 6.37,
 6.38,
 6.39,
 6.4,
 6.41,
 6.42,
 6.43,
 6.44,
 6.45,
 6.46,
 6.47,
 6.48,
 6.49,
 6.5,
 6.51,
 6.52,
 6.53,
 6.54,
 6.55,
 6.56,
 6.57,
 6.58,
 6.59,
 6.6,
 6.61,
 6.62,
 6.63,
 6.64,
 6.65,
 6.66,
 6.67,
 6.68,
 6.69,
 6.7,
 6.71,
 6.72,
 6.73,
 6.74,
 6.75,
 6.76,
 6.77,
 6.78,
 6.79,
 6.8,
 6.81,
 6.82,
 6.83,
 6.84,
 6.85,
 6.86,
 6.87,
 6.88,
 6.89,
 6.9,
 6.91,
 6.92,
 6.93,
 6.94,
 6.95,
 6.96,
 6.97,
 6.98,
 6.99,
 7.0,
 7.01,
 7.02,
 7.03,
 7.04,
 7.05,
 7.06,
 7.07,
 7.08,
 7.09,
 7.1,
 7.11,
 7.12,
 7.13,
 7.14,
 7.15,
 7.16,
 7.17,
 7.18,
 7.19,
 7.2,
 7.21,
 7.22,
 7.23,
 7.24,
 7.25,
 7.26,
 7.27,
 7.28,
 7.29,
 7.3,
 7.31,
 7.32,
 7.33,
 7.34,
 7.35,
 7.36,
 7.37,
 7.38,
 7.39,
 7.4,
 7.41,
 7.42,
 7.43,
 7.44,
 7.45,
 7.46,
 7.47,
 7.48,
 7.49,
 7.5,
 7.51,
 7.52,
 7.53,
 7.54,
 7.55,
 7.56,
 7.57,
 7.58,
 7.59,
 7.6,
 7.61,
 7.62,
 7.63,
 7.64,
 7.65,
 7.66,
 7.67,
 7.68,
 7.69,
 7.7,
 7.71,
 7.72,
 7.73,
 7.74,
 7.75,
 7.76,
 7.77,
 7.78,
 7.79,
 7.8,
 7.81,
 7.82,
 7.83,
 7.84,
 7.85,
 7.86,
 7.87,
 7.88,
 7.89,
 7.9,
 7.91,
 7.92,
 7.93,
 7.94,
 7.95,
 7.96,
 7.97,
 7.98,
 7.99,
 8.0,
 8.01,
 8.02,
 8.03,
 8.04,
 8.05,
 8.06,
 8.07,
 8.08,
 8.09,
 8.1,
 8.11,
 8.12,
 8.13,
 8.14,
 8.15,
 8.16,
 8.17,
 8.18,
 8.19,
 8.2,
 8.21,
 8.22,
 8.23,
 8.24,
 8.25,
 8.26,
 8.27,
 8.28,
 8.29,
 8.3,
 8.31,
 8.32,
 8.33,
 8.34,
 8.35,
 8.36,
 8.37,
 8.38,
 8.39,
 8.4,
 8.41,
 8.42,
 8.43,
 8.44,
 8.45,
 8.46,
 8.47,
 8.48,
 8.49,
 8.5,
 8.51,
 8.52,
 8.53,
 8.54,
 8.55,
 8.56,
 8.57,
 8.58,
 8.59,
 8.6,
 8.61,
 8.62,
 8.63,
 8.64,
 8.65,
 8.66,
 8.67,
 8.68,
 8.69,
 8.7,
 8.71,
 8.72,
 8.73,
 8.74,
 8.75,
 8.76,
 8.77,
 8.78,
 8.79,
 8.8,
 8.81,
 8.82,
 8.83,
 8.84,
 8.85,
 8.86,
 8.87,
 8.88,
 8.89,
 8.9,
 8.91,
 8.92,
 8.93,
 8.94,
 8.95,
 8.96,
 8.97,
 8.98,
 8.99,
 9.0,
 9.01,
 9.02,
 9.03,
 9.04,
 9.05,
 9.06,
 9.07,
 9.08,
 9.09,
 9.1,
 9.11,
 9.12,
 9.13,
 9.14,
 9.15,
 9.16,
 9.17,
 9.18,
 9.19,
 9.2,
 9.21,
 9.22,
 9.23,
 9.24,
 9.25,
 9.26,
 9.27,
 9.28,
 9.29,
 9.3,
 9.31,
 9.32,
 9.33,
 9.34,
 9.35,
 9.36,
 9.37,
 9.38,
 9.39,
 9.4,
 9.41,
 9.42,
 9.43,
 9.44,
 9.45,
 9.46,
 9.47,
 9.48,
 9.49,
 9.5,
 9.51,
 9.52,
 9.53,
 9.54,
 9.55,
 9.56,
 9.57,
 9.58,
 9.59,
 9.6,
 9.61,
 9.62,
 9.63,
 9.64,
 9.65,
 9.66,
 9.67,
 9.68,
 9.69,
 9.7,
 9.71,
 9.72,
 9.73,
 9.74,
 9.75,
 9.76,
 9.77,
 9.78,
 9.79,
 9.8,
 9.81,
 9.82,
 9.83,
 9.84,
 9.85,
 9.86,
 9.87,
 9.88,
 9.89,
 9.9,
 9.91,
 9.92,
 9.93,
 9.94,
 9.95,
 9.96,
 9.97,
 9.98,
 9.99,
 10.0]

rr=Ridge()
tg={'alpha':w}

from sklearn.model_selection import GridSearchCV

cv=GridSearchCV(rr,tg,scoring='neg_mean_absolute_error',cv=4)
cvmodel=cv.fit(Xnew,Y)

cvmodel.best_params_

{'alpha': 3.09}

rr=Ridge(alpha=3.09)
model=rr.fit(xtrain,ytrain)
tr_pred=model.predict(xtrain)
ts_pred=model.predict(xtest)

tr_err=mean_squared_error(ytrain,tr_pred)
ts_err=mean_squared_error(ytest,ts_pred)
tr_ab=mean_absolute_error(ytrain,tr_pred)
ts_ab=mean_absolute_error(ytest,ts_pred)

print('train_err',tr_err)
print('test_err',ts_err)
print('train_ab',tr_ab)
print('test_ab',ts_ab)

train_err 244994310.80139524
test_err 443235239.3342266
train_ab 10911.75380887483
test_ab 13737.790741817766

cvmodel.best_estimator_

Ridge(alpha=3.09)

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

Testing DataSet

df2=pd.read_csv('Downloads/testing_set.csv')

df2

	Id	MSSubClass	MSZoning	LotFrontage	LotArea	Street	Alley	LotShape	LandContour	Utilities	...	ScreenPorch	PoolArea	PoolQC	Fence	MiscFeature	MiscVal	MoSold	YrSold	SaleType	SaleCondition
0	1461	20	RH	80.0	11622	Pave	NaN	Reg	Lvl	AllPub	...	120	0	NaN	MnPrv	NaN	0	6	2010	WD	Normal
1	1462	20	RL	81.0	14267	Pave	NaN	IR1	Lvl	AllPub	...	0	0	NaN	NaN	Gar2	12500	6	2010	WD	Normal
2	1463	60	RL	74.0	13830	Pave	NaN	IR1	Lvl	AllPub	...	0	0	NaN	MnPrv	NaN	0	3	2010	WD	Normal
3	1464	60	RL	78.0	9978	Pave	NaN	IR1	Lvl	AllPub	...	0	0	NaN	NaN	NaN	0	6	2010	WD	Normal
4	1465	120	RL	43.0	5005	Pave	NaN	IR1	HLS	AllPub	...	144	0	NaN	NaN	NaN	0	1	2010	WD	Normal
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1454	2915	160	RM	21.0	1936	Pave	NaN	Reg	Lvl	AllPub	...	0	0	NaN	NaN	NaN	0	6	2006	WD	Normal
1455	2916	160	RM	21.0	1894	Pave	NaN	Reg	Lvl	AllPub	...	0	0	NaN	NaN	NaN	0	4	2006	WD	Abnorml
1456	2917	20	RL	160.0	20000	Pave	NaN	Reg	Lvl	AllPub	...	0	0	NaN	NaN	NaN	0	9	2006	WD	Abnorml
1457	2918	85	RL	62.0	10441	Pave	NaN	Reg	Lvl	AllPub	...	0	0	NaN	MnPrv	Shed	700	7	2006	WD	Normal
1458	2919	60	RL	74.0	9627	Pave	NaN	Reg	Lvl	AllPub	...	0	0	NaN	NaN	NaN	0	11	2006	WD	Normal

1459 rows × 80 columns

df2.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 1459 entries, 0 to 1458
Data columns (total 80 columns):
 #   Column         Non-Null Count  Dtype  
---  ------         --------------  -----  
 0   Id             1459 non-null   int64  
 1   MSSubClass     1459 non-null   int64  
 2   MSZoning       1455 non-null   object 
 3   LotFrontage    1232 non-null   float64
 4   LotArea        1459 non-null   int64  
 5   Street         1459 non-null   object 
 6   Alley          107 non-null    object 
 7   LotShape       1459 non-null   object 
 8   LandContour    1459 non-null   object 
 9   Utilities      1457 non-null   object 
 10  LotConfig      1459 non-null   object 
 11  LandSlope      1459 non-null   object 
 12  Neighborhood   1459 non-null   object 
 13  Condition1     1459 non-null   object 
 14  Condition2     1459 non-null   object 
 15  BldgType       1459 non-null   object 
 16  HouseStyle     1459 non-null   object 
 17  OverallQual    1459 non-null   int64  
 18  OverallCond    1459 non-null   int64  
 19  YearBuilt      1459 non-null   int64  
 20  YearRemodAdd   1459 non-null   int64  
 21  RoofStyle      1459 non-null   object 
 22  RoofMatl       1459 non-null   object 
 23  Exterior1st    1458 non-null   object 
 24  Exterior2nd    1458 non-null   object 
 25  MasVnrType     1443 non-null   object 
 26  MasVnrArea     1444 non-null   float64
 27  ExterQual      1459 non-null   object 
 28  ExterCond      1459 non-null   object 
 29  Foundation     1459 non-null   object 
 30  BsmtQual       1415 non-null   object 
 31  BsmtCond       1414 non-null   object 
 32  BsmtExposure   1415 non-null   object 
 33  BsmtFinType1   1417 non-null   object 
 34  BsmtFinSF1     1458 non-null   float64
 35  BsmtFinType2   1417 non-null   object 
 36  BsmtFinSF2     1458 non-null   float64
 37  BsmtUnfSF      1458 non-null   float64
 38  TotalBsmtSF    1458 non-null   float64
 39  Heating        1459 non-null   object 
 40  HeatingQC      1459 non-null   object 
 41  CentralAir     1459 non-null   object 
 42  Electrical     1459 non-null   object 
 43  1stFlrSF       1459 non-null   int64  
 44  2ndFlrSF       1459 non-null   int64  
 45  LowQualFinSF   1459 non-null   int64  
 46  GrLivArea      1459 non-null   int64  
 47  BsmtFullBath   1457 non-null   float64
 48  BsmtHalfBath   1457 non-null   float64
 49  FullBath       1459 non-null   int64  
 50  HalfBath       1459 non-null   int64  
 51  BedroomAbvGr   1459 non-null   int64  
 52  KitchenAbvGr   1459 non-null   int64  
 53  KitchenQual    1458 non-null   object 
 54  TotRmsAbvGrd   1459 non-null   int64  
 55  Functional     1457 non-null   object 
 56  Fireplaces     1459 non-null   int64  
 57  FireplaceQu    729 non-null    object 
 58  GarageType     1383 non-null   object 
 59  GarageYrBlt    1381 non-null   float64
 60  GarageFinish   1381 non-null   object 
 61  GarageCars     1458 non-null   float64
 62  GarageArea     1458 non-null   float64
 63  GarageQual     1381 non-null   object 
 64  GarageCond     1381 non-null   object 
 65  PavedDrive     1459 non-null   object 
 66  WoodDeckSF     1459 non-null   int64  
 67  OpenPorchSF    1459 non-null   int64  
 68  EnclosedPorch  1459 non-null   int64  
 69  3SsnPorch      1459 non-null   int64  
 70  ScreenPorch    1459 non-null   int64  
 71  PoolArea       1459 non-null   int64  
 72  PoolQC         3 non-null      object 
 73  Fence          290 non-null    object 
 74  MiscFeature    51 non-null     object 
 75  MiscVal        1459 non-null   int64  
 76  MoSold         1459 non-null   int64  
 77  YrSold         1459 non-null   int64  
 78  SaleType       1458 non-null   object 
 79  SaleCondition  1459 non-null   object 
dtypes: float64(11), int64(26), object(43)
memory usage: 912.0+ KB

df2.shape

(1459, 80)

Filling Null Values

df2.isna().sum()

Id                 0
MSSubClass         0
MSZoning           4
LotFrontage      227
LotArea            0
                ... 
MiscVal            0
MoSold             0
YrSold             0
SaleType           1
SaleCondition      0
Length: 80, dtype: int64

for i in df2.columns:
        if (df2[i].isna().sum())>0:
            if df2[i].dtypes=='object':
                x=df2[i].mode()[0]
                df2[i]=df2[i].fillna(x)
            else:
                x=df2[i].mean()
                df2[i]=df2[i].fillna(x)

df2.isna().sum()

Id               0
MSSubClass       0
MSZoning         0
LotFrontage      0
LotArea          0
                ..
MiscVal          0
MoSold           0
YrSold           0
SaleType         0
SaleCondition    0
Length: 80, dtype: int64

Dropping Irrelevant Coloumns

Seperating Input & Output Features

Xts=df2.drop(labels=['Id','LowQualFinSF','MiscVal'],axis=1)

Xts.shape

(1459, 77)

Seperating Categorical and Continous Coloumns

cat_ts=[]
con_ts=[]
for i in Xts.columns:
    if Xts[i].dtypes=='object':
        cat_ts.append(i)
    else:
        con_ts.append(i)
print(cat_ts)
print(con_ts)

['MSZoning', 'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood', 'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 'MasVnrType', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual', 'Functional', 'FireplaceQu', 'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'PavedDrive', 'PoolQC', 'Fence', 'MiscFeature', 'SaleType', 'SaleCondition']
['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'GrLivArea', 'BsmtFullBath', 'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageYrBlt', 'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 'MoSold', 'YrSold']

cat_ts=['MSZoning', 'Street', 'Alley', 'LotShape', 'LandContour', 'Utilities', 'LotConfig', 'LandSlope', 'Neighborhood', 
        'Condition1', 'Condition2', 'BldgType', 'HouseStyle', 'RoofStyle', 'RoofMatl', 'Exterior1st', 'Exterior2nd', 
        'MasVnrType', 'ExterQual', 'ExterCond', 'Foundation', 'BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 
        'BsmtFinType2', 'Heating', 'HeatingQC', 'CentralAir', 'Electrical', 'KitchenQual', 'Functional', 'FireplaceQu', 
        'GarageType', 'GarageFinish', 'GarageQual', 'GarageCond', 'PavedDrive', 'PoolQC', 'Fence', 'MiscFeature', 'SaleType', 
        'SaleCondition']
con_ts=['MSSubClass', 'LotFrontage', 'LotArea', 'OverallQual', 'OverallCond', 'YearBuilt', 'YearRemodAdd', 'MasVnrArea', 
        'BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF', 'TotalBsmtSF', '1stFlrSF', '2ndFlrSF', 'GrLivArea', 'BsmtFullBath', 
        'BsmtHalfBath', 'FullBath', 'HalfBath', 'BedroomAbvGr', 'KitchenAbvGr', 'TotRmsAbvGrd', 'Fireplaces', 'GarageYrBlt', 
        'GarageCars', 'GarageArea', 'WoodDeckSF', 'OpenPorchSF', 'EnclosedPorch', '3SsnPorch', 'ScreenPorch', 'PoolArea', 
        'MoSold', 'YrSold']

Standardisation

from sklearn.preprocessing import StandardScaler
ss=StandardScaler()

X1ts=pd.DataFrame(ss.fit_transform(Xts[con_ts]),columns=con_ts)

X1ts

	MSSubClass	LotFrontage	LotArea	OverallQual	OverallCond	YearBuilt	YearRemodAdd	MasVnrArea	BsmtFinSF1	BsmtFinSF2	...	GarageCars	GarageArea	WoodDeckSF	OpenPorchSF	EnclosedPorch	3SsnPorch	ScreenPorch	PoolArea	MoSold	YrSold
0	-0.874711	0.555587	0.363929	-0.751101	0.400766	-0.340945	-1.072885	-0.570108	0.063295	0.517348	...	-0.988013	1.185945	0.366678	-0.701628	-0.360738	-0.088827	1.818960	-0.057227	-0.038281	1.713905
1	-0.874711	0.604239	0.897861	-0.054877	0.400766	-0.439695	-1.214908	0.041273	1.063392	-0.297903	...	-0.988013	-0.741213	2.347867	-0.178826	-0.360738	-0.088827	-0.301543	-0.057227	-0.038281	1.713905
2	0.061351	0.263676	0.809646	-0.751101	-0.497418	0.844059	0.678742	-0.570108	0.773254	-0.297903	...	0.301623	0.042559	0.930495	-0.207871	-0.360738	-0.088827	-0.301543	-0.057227	-1.140614	1.713905
3	0.061351	0.458284	0.032064	-0.054877	0.400766	0.876976	0.678742	-0.456889	0.357829	-0.297903	...	0.301623	-0.012766	2.089451	-0.178826	-0.360738	-0.088827	-0.301543	-0.057227	-0.038281	1.713905
4	1.465443	-1.244533	-0.971808	1.337571	-0.497418	0.679475	0.394694	-0.570108	-0.387298	-0.297903	...	0.301623	0.153210	-0.729632	0.489198	-0.360738	-0.088827	2.243060	-0.057227	-1.875504	1.713905
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1454	2.401505	-2.314875	-1.591330	-1.447325	1.298950	-0.044694	-0.646813	-0.570108	-0.965376	-0.297903	...	-2.277648	-2.179665	-0.729632	-0.701628	-0.360738	-0.088827	-0.301543	-0.057227	-0.038281	-1.359958
1455	2.401505	-2.314875	-1.599808	-1.447325	-0.497418	-0.044694	-0.646813	-0.570108	-0.411477	-0.297903	...	-0.988013	-0.861084	-0.729632	-0.353093	-0.360738	-0.088827	-0.301543	-0.057227	-0.773170	-1.359958
1456	-0.874711	4.447740	2.055150	-0.751101	1.298950	-0.373861	0.584059	-0.570108	1.724994	-0.297903	...	0.301623	0.475939	2.982161	-0.701628	-0.360738	-0.088827	-0.301543	-0.057227	1.064053	-1.359958
1457	0.646389	-0.320147	0.125527	-0.751101	-0.497418	0.679475	0.394694	-0.570108	-0.224645	-0.297903	...	-2.277648	-2.179665	-0.103169	-0.236915	-0.360738	-0.088827	-0.301543	-0.057227	0.329164	-1.359958
1458	0.061351	0.263676	-0.038790	0.641347	-0.497418	0.712392	0.489377	-0.037980	0.700719	-0.297903	...	1.591258	0.817111	0.758218	-0.004559	-0.360738	-0.088827	-0.301543	-0.057227	1.798942	-1.359958

1459 rows × 34 columns

Encoding ( converting Categorical cloumns in continous)

ONE HOT ENCODING

X2ts=pd.get_dummies(Xts[cat_ts])

X2ts

	MSZoning_C (all)	MSZoning_FV	MSZoning_RH	MSZoning_RL	MSZoning_RM	Street_Grvl	Street_Pave	Alley_Grvl	Alley_Pave	LotShape_IR1	...	SaleType_ConLw	SaleType_New	SaleType_Oth	SaleType_WD	SaleCondition_Abnorml	SaleCondition_AdjLand	SaleCondition_Alloca	SaleCondition_Family	SaleCondition_Normal	SaleCondition_Partial
0	0	0	1	0	0	0	1	1	0	0	...	0	0	0	1	0	0	0	0	1	0
1	0	0	0	1	0	0	1	1	0	1	...	0	0	0	1	0	0	0	0	1	0
2	0	0	0	1	0	0	1	1	0	1	...	0	0	0	1	0	0	0	0	1	0
3	0	0	0	1	0	0	1	1	0	1	...	0	0	0	1	0	0	0	0	1	0
4	0	0	0	1	0	0	1	1	0	1	...	0	0	0	1	0	0	0	0	1	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1454	0	0	0	0	1	0	1	1	0	0	...	0	0	0	1	0	0	0	0	1	0
1455	0	0	0	0	1	0	1	1	0	0	...	0	0	0	1	1	0	0	0	0	0
1456	0	0	0	1	0	0	1	1	0	0	...	0	0	0	1	1	0	0	0	0	0
1457	0	0	0	1	0	0	1	1	0	0	...	0	0	0	1	0	0	0	0	1	0
1458	0	0	0	1	0	0	1	1	0	0	...	0	0	0	1	0	0	0	0	1	0

1459 rows × 234 columns

Join categorical and Continous columns

Xnew_ts=X1ts.join(X2ts)

Xnew_ts.shape

(1459, 268)

Reindexing

Xnew_ts.index=range(0,1459)

keep=[]
drop=[]
for i in Xnew_ts.columns:
    if i in Xnew.columns:
        keep.append(i)
    else:
        drop.append(i)

keep

['MSSubClass',
 'LotArea',
 'OverallQual',
 'OverallCond',
 'YearBuilt',
 'BsmtFinSF1',
 'BsmtFinSF2',
 'TotalBsmtSF',
 '2ndFlrSF',
 'GrLivArea',
 'BsmtHalfBath',
 'FullBath',
 'KitchenAbvGr',
 'Fireplaces',
 'GarageCars',
 'GarageArea',
 'WoodDeckSF',
 'OpenPorchSF',
 'MSZoning_C (all)',
 'MSZoning_FV',
 'LandContour_Bnk',
 'LandContour_HLS',
 'LandContour_Low',
 'LandContour_Lvl',
 'Utilities_AllPub',
 'LotConfig_Corner',
 'LotConfig_CulDSac',
 'LotConfig_Inside',
 'Neighborhood_Blmngtn',
 'Neighborhood_CollgCr',
 'Neighborhood_Crawfor',
 'Neighborhood_Edwards',
 'Neighborhood_Gilbert',
 'Neighborhood_MeadowV',
 'Neighborhood_Mitchel',
 'Neighborhood_NAmes',
 'Neighborhood_NPkVill',
 'Neighborhood_NWAmes',
 'Neighborhood_NoRidge',
 'Neighborhood_NridgHt',
 'Neighborhood_OldTown',
 'Neighborhood_Sawyer',
 'Neighborhood_SawyerW',
 'Neighborhood_StoneBr',
 'Neighborhood_Timber',
 'Condition1_Artery',
 'Condition1_RRAe',
 'Condition1_RRAn',
 'BldgType_Duplex',
 'HouseStyle_2Story',
 'HouseStyle_SFoyer',
 'HouseStyle_SLvl',
 'RoofStyle_Hip',
 'RoofStyle_Mansard',
 'RoofMatl_CompShg',
 'Exterior1st_AsbShng',
 'Exterior1st_BrkFace',
 'Exterior1st_CBlock',
 'Exterior1st_CemntBd',
 'Exterior1st_HdBoard',
 'Exterior1st_Wd Sdng',
 'Exterior2nd_BrkFace',
 'Exterior2nd_CBlock',
 'Exterior2nd_CmentBd',
 'Exterior2nd_HdBoard',
 'Exterior2nd_ImStucc',
 'Exterior2nd_MetalSd',
 'Exterior2nd_Stucco',
 'Exterior2nd_VinylSd',
 'Exterior2nd_Wd Sdng',
 'MasVnrType_Stone',
 'ExterQual_Ex',
 'ExterCond_TA',
 'BsmtQual_Ex',
 'BsmtQual_Fa',
 'BsmtQual_Gd',
 'BsmtCond_Gd',
 'BsmtCond_TA',
 'BsmtExposure_Av',
 'BsmtExposure_Gd',
 'BsmtFinType1_ALQ',
 'BsmtFinType1_GLQ',
 'BsmtFinType1_Unf',
 'BsmtFinType2_ALQ',
 'BsmtFinType2_GLQ',
 'BsmtFinType2_Unf',
 'Heating_Wall',
 'CentralAir_N',
 'CentralAir_Y',
 'KitchenQual_Ex',
 'Functional_Min1',
 'Functional_Min2',
 'Functional_Typ',
 'FireplaceQu_Gd',
 'FireplaceQu_Po',
 'FireplaceQu_TA',
 'GarageType_Attchd',
 'GarageType_CarPort',
 'GarageFinish_Fin',
 'GarageFinish_RFn',
 'GarageFinish_Unf',
 'GarageQual_TA',
 'GarageCond_Fa',
 'GarageCond_Gd',
 'Fence_GdPrv',
 'Fence_GdWo',
 'Fence_MnPrv',
 'SaleType_CWD',
 'SaleCondition_Abnorml',
 'SaleCondition_Alloca',
 'SaleCondition_Partial']

drop

['LotFrontage',
 'YearRemodAdd',
 'MasVnrArea',
 'BsmtUnfSF',
 '1stFlrSF',
 'BsmtFullBath',
 'HalfBath',
 'BedroomAbvGr',
 'TotRmsAbvGrd',
 'GarageYrBlt',
 'EnclosedPorch',
 '3SsnPorch',
 'ScreenPorch',
 'PoolArea',
 'MoSold',
 'YrSold',
 'MSZoning_RH',
 'MSZoning_RL',
 'MSZoning_RM',
 'Street_Grvl',
 'Street_Pave',
 'Alley_Grvl',
 'Alley_Pave',
 'LotShape_IR1',
 'LotShape_IR2',
 'LotShape_IR3',
 'LotShape_Reg',
 'LotConfig_FR2',
 'LotConfig_FR3',
 'LandSlope_Gtl',
 'LandSlope_Mod',
 'LandSlope_Sev',
 'Neighborhood_Blueste',
 'Neighborhood_BrDale',
 'Neighborhood_BrkSide',
 'Neighborhood_ClearCr',
 'Neighborhood_IDOTRR',
 'Neighborhood_SWISU',
 'Neighborhood_Somerst',
 'Neighborhood_Veenker',
 'Condition1_Feedr',
 'Condition1_Norm',
 'Condition1_PosA',
 'Condition1_PosN',
 'Condition1_RRNe',
 'Condition1_RRNn',
 'Condition2_Artery',
 'Condition2_Feedr',
 'Condition2_Norm',
 'Condition2_PosA',
 'Condition2_PosN',
 'BldgType_1Fam',
 'BldgType_2fmCon',
 'BldgType_Twnhs',
 'BldgType_TwnhsE',
 'HouseStyle_1.5Fin',
 'HouseStyle_1.5Unf',
 'HouseStyle_1Story',
 'HouseStyle_2.5Unf',
 'RoofStyle_Flat',
 'RoofStyle_Gable',
 'RoofStyle_Gambrel',
 'RoofStyle_Shed',
 'RoofMatl_Tar&Grv',
 'RoofMatl_WdShake',
 'RoofMatl_WdShngl',
 'Exterior1st_AsphShn',
 'Exterior1st_BrkComm',
 'Exterior1st_MetalSd',
 'Exterior1st_Plywood',
 'Exterior1st_Stucco',
 'Exterior1st_VinylSd',
 'Exterior1st_WdShing',
 'Exterior2nd_AsbShng',
 'Exterior2nd_AsphShn',
 'Exterior2nd_Brk Cmn',
 'Exterior2nd_Plywood',
 'Exterior2nd_Stone',
 'Exterior2nd_Wd Shng',
 'MasVnrType_BrkCmn',
 'MasVnrType_BrkFace',
 'MasVnrType_None',
 'ExterQual_Fa',
 'ExterQual_Gd',
 'ExterQual_TA',
 'ExterCond_Ex',
 'ExterCond_Fa',
 'ExterCond_Gd',
 'ExterCond_Po',
 'Foundation_BrkTil',
 'Foundation_CBlock',
 'Foundation_PConc',
 'Foundation_Slab',
 'Foundation_Stone',
 'Foundation_Wood',
 'BsmtQual_TA',
 'BsmtCond_Fa',
 'BsmtCond_Po',
 'BsmtExposure_Mn',
 'BsmtExposure_No',
 'BsmtFinType1_BLQ',
 'BsmtFinType1_LwQ',
 'BsmtFinType1_Rec',
 'BsmtFinType2_BLQ',
 'BsmtFinType2_LwQ',
 'BsmtFinType2_Rec',
 'Heating_GasA',
 'Heating_GasW',
 'Heating_Grav',
 'HeatingQC_Ex',
 'HeatingQC_Fa',
 'HeatingQC_Gd',
 'HeatingQC_Po',
 'HeatingQC_TA',
 'Electrical_FuseA',
 'Electrical_FuseF',
 'Electrical_FuseP',
 'Electrical_SBrkr',
 'KitchenQual_Fa',
 'KitchenQual_Gd',
 'KitchenQual_TA',
 'Functional_Maj1',
 'Functional_Maj2',
 'Functional_Mod',
 'Functional_Sev',
 'FireplaceQu_Ex',
 'FireplaceQu_Fa',
 'GarageType_2Types',
 'GarageType_Basment',
 'GarageType_BuiltIn',
 'GarageType_Detchd',
 'GarageQual_Fa',
 'GarageQual_Gd',
 'GarageQual_Po',
 'GarageCond_Ex',
 'GarageCond_Po',
 'GarageCond_TA',
 'PavedDrive_N',
 'PavedDrive_P',
 'PavedDrive_Y',
 'PoolQC_Ex',
 'PoolQC_Gd',
 'Fence_MnWw',
 'MiscFeature_Gar2',
 'MiscFeature_Othr',
 'MiscFeature_Shed',
 'SaleType_COD',
 'SaleType_Con',
 'SaleType_ConLD',
 'SaleType_ConLI',
 'SaleType_ConLw',
 'SaleType_New',
 'SaleType_Oth',
 'SaleType_WD',
 'SaleCondition_AdjLand',
 'SaleCondition_Family',
 'SaleCondition_Normal']

len(keep)

111

len(Xnew_ts.columns)

268

Xnewtest=Xnew_ts[keep]

len(Xnewtest.columns)

111

Xnewtest

	MSSubClass	LotArea	OverallQual	OverallCond	YearBuilt	BsmtFinSF1	BsmtFinSF2	TotalBsmtSF	2ndFlrSF	GrLivArea	...	GarageQual_TA	GarageCond_Fa	GarageCond_Gd	Fence_GdPrv	Fence_GdWo	Fence_MnPrv	SaleType_CWD	SaleCondition_Abnorml	SaleCondition_Alloca	SaleCondition_Partial
0	-0.874711	0.363929	-0.751101	0.400766	-0.340945	0.063295	0.517348	-0.370808	-0.775254	-1.215588	...	1	0	0	0	0	1	0	0	0	0
1	-0.874711	0.897861	-0.054877	0.400766	-0.439695	1.063392	-0.297903	0.639144	-0.775254	-0.323539	...	1	0	0	0	0	1	0	0	0	0
2	0.061351	0.809646	-0.751101	-0.497418	0.844059	0.773254	-0.297903	-0.266876	0.891944	0.294508	...	1	0	0	0	0	1	0	0	0	0
3	0.061351	0.032064	-0.054877	0.400766	0.876976	0.357829	-0.297903	-0.271395	0.837243	0.243004	...	1	0	0	0	0	1	0	0	0	0
4	1.465443	-0.971808	1.337571	-0.497418	0.679475	-0.387298	-0.297903	0.528434	-0.775254	-0.424487	...	1	0	0	0	0	1	0	0	0	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
1454	2.401505	-1.591330	-1.447325	1.298950	-0.044694	-0.965376	-0.297903	-1.129968	0.523306	-0.811797	...	1	0	0	0	0	1	0	0	0	0
1455	2.401505	-1.599808	-1.447325	-0.497418	-0.044694	-0.411477	-0.297903	-1.129968	0.523306	-0.811797	...	1	0	0	0	0	1	0	1	0	0
1456	-0.874711	2.055150	-0.751101	1.298950	-0.373861	1.724994	-0.297903	0.401907	-0.775254	-0.539856	...	1	0	0	0	0	1	0	1	0	0
1457	0.646389	0.125527	-0.751101	-0.497418	0.679475	-0.224645	-0.297903	-0.303026	-0.775254	-1.063136	...	1	0	0	0	0	1	0	0	0	0
1458	0.061351	-0.038790	0.641347	-0.497418	0.712392	0.700719	-0.297903	-0.113237	1.612573	1.058827	...	1	0	0	0	0	1	0	0	0	0

1459 rows × 111 columns

pred=cvmodel.predict(Xnewtest)

pred

array([128619.54163418, 181267.41238455, 203420.31328191, ...,
       194632.16524631, 113066.61351441, 220273.64798307])

predn={'Output':list(pred)}

predn

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ID=df2['Id']

ID

0       1461
1       1462
2       1463
3       1464
4       1465
        ... 
1454    2915
1455    2916
1456    2917
1457    2918
1458    2919
Name: Id, Length: 1459, dtype: int64

df_final1=pd.DataFrame(ID)

df_final1

	Id
0	1461
1	1462
2	1463
3	1464
4	1465
...	...
1454	2915
1455	2916
1456	2917
1457	2918
1458	2919

1459 rows × 1 columns

df_final2=pd.DataFrame(predn)

df_final2

	Output
0	128619.541634
1	181267.412385
2	203420.313282
3	208505.126292
4	193287.829533
...	...
1454	80304.088670
1455	79292.599894
1456	194632.165246
1457	113066.613514
1458	220273.647983

1459 rows × 1 columns

Final Output

df_final=df_final1.join(df_final2)

df_final

	Id	Output
0	1461	128619.541634
1	1462	181267.412385
2	1463	203420.313282
3	1464	208505.126292
4	1465	193287.829533
...	...	...
1454	2915	80304.088670
1455	2916	79292.599894
1456	2917	194632.165246
1457	2918	113066.613514
1458	2919	220273.647983

1459 rows × 2 columns